diff --git "a/Analytics_vidhya_final_data.csv" "b/Analytics_vidhya_final_data.csv" new file mode 100644--- /dev/null +++ "b/Analytics_vidhya_final_data.csv" @@ -0,0 +1,3186 @@ +Title,Course Curriculum,Course Description +Creating Problem-Solving Agents using GenAI for Action Composition,"Creating Problem-Solving Agents using GenAI for Action Composition +- Introduction +- Overview- Count the Number of Agents +- A brief history of Agentic Systems +- Agents Today +- Multi-Agent Systems Today +- Practical Solutions","This introductory course provides a concise overview of Agentic AI systems, covering their evolution, current state, and practical applications. You will explore key topics including the history of Agentic AI systems, the role of agents today, multi-agent systems, and practical solutions for implementing them. Perfect for those seeking a foundational understanding of intelligent Agentic AI systems in action." +Improving Real World RAG Systems: Key Challenges & Practical Solutions,"Improving Real World RAG System +- Introduction to RAG Systems +- Resources +- RAG System Challenges Practical Solutions +- Hands-on: Solution for Missing Content in RAG +- Other Key Challenges +- Practical Solutions +- Hands-on: Solution for Missed Top Ranked, Not in Context, Not Extracted _ Incorrect SpecificityHands-on- Solution for Missed +- Wrong Format Problem Solution +- Hands-on: Solution for Wrong Format +- Incomplete Problem Solution +- HyDE +- Other Practical Solutions from recent Research Papers","This course explores the key challenges in building real-world Retrieval-Augmented Generation (RAG) systems and provides practical solutions. Topics include improving data retrieval, dealing with hallucinations, context selection, and optimizing system performance using advanced prompting, retrieval strategies, and evaluation techniques. Through hands-on demos, you will gain insights into better chunking, embedding models, and agentic RAG systems for more robust, real-world applications." +Framework to Choose the Right LLM for your Business,"Introduction +- Introduction +It's an LLM World! +- It's an LLM World! +Understand Your Business +- Understand Your Business +Framework to Choose the Right LLM +- Framework to Choose the Right LLM +Case Studies +- Case Studies +Conclusion +- Conclusion","This course will guide you through the process of selecting the most suitable Large Language Model (LLM) for various business needs. By examining factors such as accuracy, cost, scalability, and integration, you will understand how different LLMs perform in specific scenarios, from customer support to healthcare and strategy development. The course emphasizes practical decision-making with real-world case studies, helping businesses navigate the rapidly evolving LLM landscape effectively." +Building Smarter LLMs with Mamba and State Space Model,"Course Overview +- Course Overview +An Alternative to Transformers +- Are RNNs a Solution +- The Problem with Transformers +Understanding State Space Models +- What is a State Space Model? +- The Discrete Representation +- The Recurrent Representation +- The Convolution Representation +- The Three Representations +- The Importance of the A Matrix +Mamba - A Selective State Space Model +- What Problem does it attempt to Solve? +- Selectively Retaining Information +- Speeding Up Computations +- Exploring the Mamba Block +- Jamba - Mixing Mamba with Transformers","Unlock the Power of State Space Models (SSM) like Mamba with our comprehensive course designed for AI professionals, data scientists, and NLP enthusiasts. Master the art of integrating SSM with deep learning, unravel the complexities of models like Mamba, and elevate your understanding of Generative AI's newest and most innovative models. This course is designed to equip you with the skills needed to understand these cutting-edge AI models and how they work, making you proficient in the latest AI techniques and architectures." +Generative AI - A Way of Life - Free Course,"Introduction to Generative AI +- Fundamentals of Generative AI +- What is Generative AI? +- How does Generative AI work? +- Exploring the Potential of Generative AI +- GenAI Pinnacle Program +- Hands On: Let’s get generating! +Text Generation Using Generative AI +- An Overview of Text Generation +- What is ChatGPT? +- Working with ChatGPT +- Working with ChatGPT Plus +- Working with Bing Chat +- Breaking Bard +- Gen AI Pinnacle Ad +- Learning the Art of Prompting +- Creating a Chatbot +- Ethics and Best Practices +Image Generation Using Generative AI +- Introduction to Image Generation +- Exploring the Potential of Image Generation +- Working with free image generation tools +- Working with Clipdrop +- Working with Bing Image Creator +- Working with Firefly +- Working with Paid Image Generative Tools +- Working with paid image generative tools DreamStudio +- Working with DALLE-2 +- Working with Midjourney +- Gen AI Pinnacle Ad +- Prompting your Way to Art +- Accomplishing Tasks with Image Generation +- E for Ethics and Efficiency","Generative AI - A Way of LifeThis course is a transformative journey tailored for beginners and delves into AI-powered text and image generation using leading tools like ChatGPT, Microsoft Copilot, and DALL·E3. Learn practical applications across industries, ethical considerations, and best practices. Whether you're a content creator, business innovator, or AI enthusiast, gain the expertise to harness Generative AI's full potential and drive innovation in your field." +Building LLM Applications using Prompt Engineering - Free Course,"How to build diffferent LLM AppIications? +- Introduction to Building Different LLM applications +- Prompt Engineering +- Retrieval Augmented Generation +- Finetuning LLMs +- Training LLMs from Scratch +- Quiz +Getting Started with Prompt Engineering +- Introduction to Prompt Engineering +- Set up your machine for Prompt Engineering +- Prompt Engineering with ChatGPT API +- Enabling Conversation with ChatGPT API +- Quiz +Understanding Different Prompt Engineering Techniques +- Introduction to Understanding Different Prompt Engineering Techniques +- Few Shot Prompting +- One Shot Prompting +- Zero Shot Prompting +- Quiz +- Assignment","Who Should Enroll?This course will provide you with a hands-on understanding of building LLM applications and mastering prompt engineering techniques. By the end of the course, you will be proficient in implementing and fine-tuning these techniques to enhance generative AI model performance. You'll learn to apply various prompting methods and build chatbots on enterprise data, equipping you with the skills to improve conversational AI systems in real-world projects.Who Should Enroll:Professionals:Individuals looking to deepen their knowledge and apply advanced LLM and prompt engineering techniques to solve complex problems across various domains.Aspiring Students:Individuals looking to deepen their knowledge and apply advanced LLM and prompt engineering techniques to solve complex problems across various domains." +Building Your First Computer Vision Model - Free Course,"Introduction to Computer Vision +- Pixel Perfect - Decoding Images +- Understanding a CNN - Convolutional Layer +- Hands on - Image Processing Techniques +- Understanding a CNN - Striding and Pooling +- Understanding a CNN - Pooling Layer +- Understanding AlexNet and Building a CNN Model +- Quiz","Introduction to Computer Vision using PyTorchThis course will help you gain a deep understanding of Computer Vision and build advanced CV models using the PyTorch framework. With a carefully curated list of resources and exercises, this course is your guide to becoming a Computer Vision expert. Master the techniques to build convolutional neural networks, and classify images.Who Should Enroll:Professionals: Individuals looking to expand their skill set and leverage CV across different industries.Aspiring Students: For those setting out on their journey to master image data analysis and leave a mark in the tech world." +Bagging and Boosting ML Algorithms - Free Course,"Bagging +- Resources to be used in this course +- Problem Statement +- Understanding Ensemble Learning +- Introducing Bagging Algorithms +- Hands-on to Bagging Meta Estimator +- Introduction to Random Forest +- Understanding Out-Of-Bag Score +- Random Forest VS Classical Bagging VS Decision Tree +- Project +Boosting +- Introduction to Boosting +- AdaBoost Step-by-Step Explanation +- Hands-on - AdaBoost +- Gradient Boosting Machines (GBM) +- Hands-on Gradient Boost +- Other Algo (XGBoost, LightBoost. CatBoost) +- Project: Anova Insurance","Bagging and Boosting ML AlgorithmsThis course will provide you with a hands-on understanding of Bagging and Boosting techniques in machine learning. By the end of the course, you will be proficient in implementing and tuning these ensemble methods to enhance model performance. You'll learn to apply algorithms like Random Forest, AdaBoost, and Gradient Boosting to a real-world dataset, equipping you with the skills to improve predictive accuracy and robustness in your projects.Who Should Enroll:Professionals:Individuals looking to deepen their knowledge and apply advanced machine learning techniques like Bagging and Boosting to solve complex problems across various domainsAspiring Students:Individuals looking to deepen their knowledge and apply advanced ML techniques to bring value to businesses" +MidJourney: From Inspiration to Implementation - Free Course,"MidJourney +- MidJourney - Storm _ Story +- MidJourney - Inspiration +- MidJourney - How to use +- MidJourney Alternatives +- Quiz","MidJourney: From Inspiration to ImplementationThis course will provide you with a practical understanding of MidJourney tools. By the end of the course, you will be able to utilize MidJourney effectively and explore alternative tools for your creative projects. You'll learn how to draw inspiration, use MidJourney's features, and understand its applications through engaging lessons.Who Should Enroll:Creative Professionals:Individuals looking to enhance their creativity and apply MidJourney tools to various artistic and visual projects.Aspiring Creatives:Those beginning their journey into visual storytelling and digital art, seeking to learn the fundamentals of MidJourney and its alternatives." +Understanding Linear Regression - Free Course,"Linear Regression +- Introduction to the Problem Statement +- Resources for this Course +- Introduction to Linear Regression +- Significance of Slope and Intercept in the linear regression +- How Model Decides The Best-Fit Line +- Let’s Build a Simple Linear Regression Model +- Model Understanding Using Descriptive Approach +- Model Understanding Using Descriptive Approach - II +- Model Building Using Predictive Approach +- Quiz: Linear regression","Understanding Learning RegressionThis free course will help you understand the fundamentals of linear regression in a straightforward manner. By the end of this course, you will be able to build predictive models using linear regression techniques. With a carefully curated list of resources and exercises, this course serves as your comprehensive guide to mastering linear regression." +The Working of Neural Networks - Free Course,"Understanding the working of Neural Networks +- How are Neural Networks trained - Forward Propagation +- Understanding Loss Functions + Hands on +- Reading: Creating a Custom Loss Function (Optional) +- Optimization Techniques - Gradient Descent +- What is Back Propagation? +- Types of Gradient Descent +- Common Optimization Techniques - Part 1 +- Common Optimization Techniques - Part 2 +- Building a Deep Neural Network (Hands-on Regression Model) +- Building a Deep Neural Network (Hands-on Classification Model) +- Quiz","The Working of Neural NetworksThis free course will help you understand the end-to-end working of neural networks in a simple manner. By the end of this course, you will be able to build advanced Deep Learning models using the PyTorch framework. With a carefully curated list of resources and exercises, this course serves as your comprehensive guide to mastering deep learning. It is recommended that you complete the advanced Machine Learning course before taking up this course." +The A to Z of Unsupervised ML - Free Course,"Understanding Unsupervised Machine Learning +- Resources to be used in this course. +- Setting the Context +- Choosing Clustering Algorithms +- Solving our Problem using k-means - Part 1 +- Solving our Problem using k-means - Part 2 +- Finding optimal K value +- Analysis and Insights Based on the Plots +- Introduction to Hierarchical Clustering Analysis (HCA) +- Solving our Problem using Hierarchical Clustering +- Introduction to DBSCAN Clustering +- Solving our Problem using DBSCAN +- Reading: Applications of Clustering in the Real World +- Project","Why Unsupervised Machine Learning?Unsupervised machine learning helps uncover hidden patterns and structures in data without labeled examples. It is essential for exploratory data analysis, reducing dimensionality, and discovering intrinsic relationships within datasets. Mastering unsupervised techniques enhances data preprocessing and drives insights in complex datasets where labels are scarce or unavailable." +Building Your first RAG System using LlamaIndex - Free Course,"Introduction to RAG systems +- Welcome to this course +- Why RAG +- What is RAG system +- Overview of RAG Framework +- Quiz +- Course handouts +Getting Started with LlamaIndex +- Introduction to LlamaIndex +- Components of LlamaIndex +- Reading Material: How to get your API Key +- How to get Open AI Keys - 2 min - Website go through +- Build Your First RAG system using LlamaIndex +- Quiz","Building Your First RAG Model using LlamaIndexThis course will guide youthrough building your first Retrieval-Augmented Generation (RAG) systemusing LlamaIndex.You will start with data ingestion by loading a file into the system, followed by indexing the data for efficient retrieval. Next, you will set up retrieval configurations and use a response synthesizer to combine data into a coherent response. Finally, you will employ a query engine to generate responses. By the end of this course, you will have a solid understanding of these processes and be able to build an RAG system using LlamaIndex code effectively." +Data Preprocessing on a Real-World Problem Statement - Free Course,"Preparing the Dataset for Machine Learning Model +- Resources to be used in this course +- Introduction to Problem Statement +- Reading Material - Understanding the Data +- ML-workflow +- Tasks to be Performed +- Combining Product Attribute Data with POS Data +- Combining all the tables in the Dataframe +- Understanding the Combined Data +- Treating Missing Values - Part 1 +- Treating Missing Values Part - 2 +- Outlier Detection and Treatment +- Preparing the Dataset for Supervised and Unsupervised Models +- Generative AI for Data Analysis","Data Processing on a Real World Problem StatementThis course will help you get a practical understanding of Data Preprocessing. After this course, you can work on any data and prepare it for modelling. With a carefully curated list of resources, this course is your first step to becoming a Data Scientist. By the end of the course, you will have mastered techniques like EDA and Missing Value Treatment.Who Should Enroll:Professionals: Individuals looking to expand their skill set on data cleaning and preparation.Aspiring Students: For those setting out on their journey to become a data scientist and making a mark in the tech world." +Exploring Stability.AI - Free Course,"Mastering stability.ai and its tools +- Introduction to Stability +- How to use Stability.AI tools +- Review of Deployment Options for SD WebUI +- Automatic1111 WebUI on RunPod GPU environment +- SD WebUI Hands-On - Installation and Setup +- SD WebUI Hands-On - Generation and Settings +- Quiz","Exploring Stability.AIThis course will give you a practical understanding of Stability.AI tools. By the end of the course, you will be able to deploy and customize SD WebUI, and use the Automatic1111 WebUI on RunPod GPU environments. You'll learn to install, set up, generate, and fine-tune SD WebUI settings, equipping you with the skills to harness Stability.AI's full potential for your projects.Who Should Enroll:Professionals:Individuals aiming to enhance their skill set and apply Stability.AI tools/Stable Diffusion in various fields.Aspiring Students:Those beginning their journey to mastering Generative AI tool deployment and customization, looking to make an impact in the evolving world of Generative AI" +Building a Text Classification Model with Natural Language Processing - Free Course,"Introduction to NLP +- What is NLP +- Common tasks in a NLP Project +- NLP Libraries +- Resources for the Course +- Methods of Text Preprocessing - Part 1 +- Methods of Text Preprocessing - Part 2 +- Methods of Text Preprocessing - Part 3 +- Quiz +Building a basic classification model +- Introduction to dataset and problem statement +- Creating a Basic Review Classification Model +- Understanding TF-IDF and its implementations +- Understanding N-grams +- Advanced Preprocessing Techniques +- Building an basic ANN model +- Limitations of ANN +- Quiz","Introduction to Natural Language Processing with PyTorchGain practical insights into Natural Language Processing (NLP) with our comprehensive course. Learn to build NLP models using PyTorch, delve into classification models, and apply techniques like bag-of-words, count vectorizer and so on. Perfect for professionals seeking to enhance their skills and aspiring students entering the tech industry.Who Should Enroll:Professionals:Expand your skill set with NLP for real-world applications in diverse industries.Aspiring Students:Master text data analysis and kickstart your career in AI and NLP." +Getting Started with Large Language Models,"Introduction +- Course Objective +- Course Handouts +- The Exponential Growth +The Evolution of NLP +- The Evolution of NLP: Symbolic NLP +- The Evolution of NLP: Statistical NLP +- The Evolution of NLP: Deep Learning +- The Evolution of NLP: Deep Learning Era II +- The Evolution of NLP: Tranformers and Evolution +- Quiz +What are Large Language Models? +- Introduction to Large Language Model +- What is a Large Language Model? +- Understanding Foundational Models +- Different types of LLMs: Based on Response +- Different types of LLMs: Based on Model Architecture +- Quiz +The Current State of the Art in LLMs +- The Current State of the Art in LLMs +Generative AI - Glossary +- Generative AI- Glossary +Your Feedback Matters! +- Your Feedback Matters!","Getting Started With LLMsThis course will help you gain a comprehensive understanding of Large Language Models (LLMs) and develop advanced natural language processing (NLP) applications using the PyTorch framework. With a carefully curated list of resources and exercises, this course is your guide to becoming an expert in LLMs. Master the techniques to build and fine-tune LLMs, and generate human-like text.Who Should Enroll: Professionals: Individuals looking to expand their skill set and leverage LLMs across different industries. Aspiring Students: For those setting out on their journey to master language data analysis and leave a mark in the tech world." +Introduction to Generative AI,"Introduction to Generative AI +- What is Generative AI +- How does Gen AI work +- Quiz +- Text Generation with Gen AI +- Image Generation with Gen AI +- Quiz +- Meet your Instructors +- Course Handout +- Your Feedback Matters!","Introduction to Generative AIThis course will provide you with a comprehensive understanding of generative AI, including text and image generation techniques. By the end of the course, you will be have an understanding of using generative AI tools to create diverse content. You'll learn how generative AI works, engage in practical exercises, and gain the skills to implement these techniques in real-world projects.Who Should Enroll:Professionals:Individuals looking to enhance their skills in generative AI and apply advanced techniques to create innovative solutions across various domains.Aspiring Students:Individuals eager to enter the field of generative AI and apply generative AI techniques to tackle complex problems and generate creative content across different fields." +Nano Course: Dreambooth-Stable Diffusion for Custom Images,"Dreambooth-Stable Diffusion for Custom Images +- The Current Landscape of Generative AI +- Why Stable Diffusion +- Recap on History of Stable Diffusion +- Intuition behind Stable Diffusion +- How to train a Stable Diffusion model +- Introduction to Dreambooth +- Understanding the Dreambooth Process +- Tricks to Name Your Concept Uniquely +- How to Select Images for Finetuning Dreambooth +- Setting up the Training Environment +- Code-Finetuning Dreambooth model on Custom Dataset +- The Importance of Captioning in Dreambooth +- Differences between Stable Diffusion and Dreambooth","Nano Course: Dreambooth- Stable Diffusion for Custom ImagesHave you ever wondered how to turn your dreams into reality by creating images of your dog traveling around the world or yourself alongside Elon Musk or playing cricket with MSD?This is exactly where the dreambooth model comes into the picture. With the help of Dreambooth, you can personalize the stable diffusion for a particular subject.Given just 5 images of our subject, dreambooth can create new images across diverse scenes, poses, views, and lighting conditions that do not appear in the reference images.In this free nano course on Dreambooth, Sandeep will discuss the historical journey of stable diffusion, its current landscape, and a brief understanding of the stable diffusion training process. Then we will move on to the dreambooth, its training process and finetune dreambooth on our custom dataset." +A Comprehensive Learning Path for Deep Learning in 2023,"January 2023 +- Getting Started +- Overview of the Learning Path +- Month-on-Month Plan +- Introduction to Deep Learning +- Applications of Deep Learning +- Setting up your System +- Descriptive Statistics and Probability +- Python +- Exercise : Python +- AI&ML Blackbelt Plus Program (Sponsored) +February 2023 +- Start engaging in data science / deep learning communities +- Inferential Statistics +- Exercise : Statistics +- Partial Derivative +- Linear Algebra - Part 1 +- Linear Regression +- Logistic Regression +- Exercise : Linear and Logistic Regression +- Regularization Techniques (Ridge and Lasso) +- Project +March 2023 +- Start building your GitHub profile +- Start building your GitHub profile +- Linear Algebra - Part 2 +- Getting Started with Neural Networks +- Understanding Forward Propagation +- Understanding Back Propagation +- Exercise : Understanding Neural Networks +- Build your first Neural Network in Numpy +- Frameworks for Deep Learning +- Introduction to Keras +- Build your first Neural Network in Keras +- Project +April 2023 +- Start Participating in Competitions +- Handling / Pre-processing Images +- Exercise +- Hyperparameter Tuning +- Regularization Techniques +- Optimization Algorithms +- Exercise +- Project +May 2023 +- Understanding Convolutional Neural Networks (CNNs) +- Exercise : CNNs +- Hyperparameter Tuning +- Transfer Learning +- Data Augmentation +- Project +June 2023 +- Build your resume and apply for Internships +- Visualizing Convolutional Neural Networks +- Project 3 on CV +- Project 4 on CV +July 2023 +- Start writing articles +- Handling / Pre-processing Text Data +- Exercise +- Recurrent Neural Networks (RNNs) +- RNNs - Video +- LSTM +- GRUs +- Transfer Learning for NLP +- Project 1 on NLP +August 2023 +- Word Embeddings +- Exercise +- Project 2 on NLP +September 2023 +- Attention Models +- Attention Models - Text +- Project on Attention Models +October 2023 +- Unsupervised Deep Learning +- Project on Unsupervised Deep Learning +November 2023 +- GANs : Video +- GANs +- Project on GANs +December 2023 +- Apply for Jobs and Internships +- Way Forward",About the courseThe most common question we get from beginners in the field of Deep Learning is - Where to begin? The journey to becoming a Deep Learning expert can be difficult if one does not have the right resources to follow. There are a million resources to refer and it is tough to decide where to start from.We are here to help you take your first steps into the world of Deep Learning. Here is a free learning path for people who want to become a Deep Learning expert in 2023. We have arranged the best resources in a logical manner along with exercises to make sure that you only need to follow one single source to become a data scientist. +A Comprehensive Learning Path to Become a Data Scientist in 2024,"Overview of the Learning Path 2024 +- Overview of Learning Path +- Month-on-Month Plan +- Your Personalized Learning Path for Data Science +- AI&ML Blackbelt Plus Program (Sponsored) +January 2024: Data Science Toolkit +- Plan for January 2024 +- Understanding Machine Learning and its impact +- Job of Data Scientist +- Exercise +- Overview of the Course +- A brief introduction to Python +- Introduction to Python Test +- Installing Python +- Theory of Operators +- Exercise +- Understanding Operators in Python +- Operators Test +- Understanding variables and data types +- Variable Test +- Variables and Data Types in Python +- Understanding Conditional Statements +- Exercise +- Implementing Conditional Statements in Python +- Conditional Statements test +- Understanding Looping Constructs +- Exercise +- Implementing Looping Constructs in Python +- Looping Constructs test +- Understanding Functions +- Implementing Functions in Python +- Functions test +- A brief introduction to data structure +- Data Structure test +- Understanding the concept of Lists +- Lists test +- Implementing Lists in Python +- Exercise +- Understanding the concept of Dictionaries +- Exercise +- Implementing Dictionaries in Python +- Dictionaries test +- Understanding the concept of Standard Libraries +- Libraries test +- Reading a CSV File in Python - Introduction to Pandas +- Reading a CSV file in Python: Implementation +- Reading a csv file in Python test +- Understanding dataframes and basic operations +- DataFrames and basic operations test +- Reading dataframes and conduct basic operations in Python +- Reading dataframes and conduct basic operations in Python Test +- Indexing a Dataframe +- Indexing DataFrames test +- Exercise +- Sorting Dataframes +- Merging Dataframes +- Quiz: Sorting and Merging dataframes +- Apply function +- Aggregating data +- Quiz: Apply function and Aggregating data +- Basics of Matplotlib +- Data Visualization using Matplotlib +- Quiz: Matplotlib +- Basics of Seaborn +- Data Visualization using Seaborn +- Quiz: Seaborn +- Regular Expressions +- Understanding Regular Expressions +- Quiz: Regular Expressions +- Regular Expressions in Python +- Quiz: Regular Expressions in Python +- Cheatsheet for Python +- Instructions +- Quiz +- Python Coding Challenge +February 2024: Data Visualization +- Exercise - Definition of Data Visualization +- Why Tableau is a Powerful Tool for Professionals +- Compare Tableau Against Power BI and Qlik +- The Tableau Range of Products +- The 5 Tableau Products you should Know +- Installing Tableau Desktop on your System +- Installing Tableau Public on your System +- Difference Between Tableau Server and Tableau Online +- Navigating the Tableau Interface (Part 1) +- Navigating the Tableau Interface (Part 2) +- Connecting to Data Sources in Tableau +- Understanding the Problem Statement +- Download the Superstore Dataset +- Loading the Dataset and Getting Familiar with the Variables +- Build your First Visualization in Tableau! +- Hands-On with Labels and Formatting +- Playing Around with Colors +- Using Filters to Build a Pivot Structure in Tableau +- Exporting your Tableau Worksheet +- The Different Chart Types in Tableau +- Line Charts - Working with Time Series Data +- Building Line Charts in Tableau +- Exercise - Sales of Each Category Month-by-Month +- Generating Map Visualizations for Geospatial Analysis +- Map Visualizations in Tableau +- Exercise - Sales by City Analysis +- Bar Charts, Histograms, Scatter Plots, Bubble Charts, Pie Charts +- Dual Axis Charts in Tableau +- Date Dual Axis Charts in Tableau +- What are Calculated Fields? +- Feature Engineering in Tableau - Average Shipping Time +- Exercise - Number of Orders per State +- Calculating the Average Order Value +- Average Order Value for Product Sub-Categories +- What are Parameters in Tableau? +- Using Parameters to find Top N Customers +- Using Parameters to Analyze Superstore's Variable Values +- Joins and their Different Types in Tableau +- Performing Data Joining in Tableau +- What is Blending? How is it Different from Joins? +- Blending Data in Tableau +- Download the Coffee Chain Dataset +- Introduction to Dashboards and their Use Cases +- Reading Material - Dashboards in Tableau +- Designing your First Dashboard in Tableau +- Using Parameters to Create Dynamic Dashboards +- How to Upload your Work to the Tableau Public Gallery +- Designing the Blueprint for a Multi-Dashboard View to Analyze Sales +- Building Multiple Interlinked Dashboards in Tableau for our Business +- The Art of Storytelling +- 3-Step Storytelling Framework +- Sketching the Story Blueprint +- Profits by Region Analysis using Storyboard in Tableau +- Capstone Project: Sales and Profit by Segment using Storyboards in Tableau +- Getting started with SQL +- Types of Databases +- How data is Stored in Relational Databases +- How data is stored in NoSQL databases +- Exercise 1 +- Introduction +- Architecture: Client and Server +- MySQL Distributions +- Local Installation on Mac +- Local Installation on Linux +- Local Installation on Windows +- Licensing +- Accessing a remote MySQL server +- Graphical user interfaces +- Exercise 2 +- SQL - Installation Guide +- Introduction +- What exactly is SQL? +- History of SQL +- Connecting to MySQL +- Types of Commands - DDL (Creation/ Deletion/ Updating of Schema +- Types of Commands - DML (Manipulating data in tables) +- Types of Commands - DCL (Managing Access control) +- Exploring databases +- Creating tables +- Inserting data in tables +- SELECT Statement - Introduction +- Datatypes in MySQL +- NULL vs NOT NULL +- Exercise 3 +- Introduction +- Update command – Concept +- Update command – Example +- Delete command – Concept +- Delete command – Example +- Describe command – Concept +- Describe command – Example +- Alter command – Concept and Example +- Exercise 4 +- Introduction +- Importing data from CSV to MySQL +- Exporting data from MySQL to CSV +- Backing up databases +- Restoring databases +- Exercise 5 +- Importing and Exporting Datasets - Troubleshooting Guide +- Introduction +- Counting Rows and Items +- Aggregation Functions – SUM, AVG, STDDEV +- Extreme Values Identification – MIN, MAX +- Slicing data +- Limiting data +- Sorting data +- Filtering Patterns +- Groupings, Rolling up data and Filtering in Groups +- Exercise 6 +- Introduction +- Data Eyeballing +- Data Dictionary +- Questions we need answers of +- Analyzing data and creating table structure +- Loading data to our MySQL table +- Data Analysis – Simple Queries +- Data Analysis – Advanced Queries +- FIFA19 Players dataset (cleaned) for this Project +- Introduction +- The need for joins +- Different type of joins +- The Left Join - Concept +- The Left Join – Practical Example +- The Inner Join +- The Cross Join +- The Right Join +- The Self Join +- Exercise +- Introduction +- Introduction to Indexing +- How indexing works (basics) +- Relationships +- Types of Relationships +- Table Constraints – PRIMARY KEY, FOREIGN KEY, UNIQUENESS and AUTO INCREMENT +- Exercise +- String functions - CONCAT +- String functions – Case Conversion +- String functions – Trimming Strings +- String functions – Extracting Substrings +- Date/ Time functions – Current date and time +- Date/ Time functions – Extracting date and time from field +- Date/ Time functions – Formatting date and time as Strings +- Numeric functions +- SQL CheatSheet +- Exercise +- Introduction +- Setting up a virtual environment +- Installing the required packages +- Connecting to MySQL +- Connecting to database table and pulling data +- Querying the database- INSERT +- Querying the database- DELETE +- Querying the database- SEARCH +- Querying the database- INDEXING +- Notes and Resources +- Subscribe to Data Science Newsletter and Podcast +March 2024: Data Exploration +- Overview of Statistics +- Important applications of Statistics +- What is Descriptive Statistics? +- Introduction to Design experiments +- Introduction to Design experiments-Video +- Exercise +- Visualizing Data +- Visualizing Data +- Central tendency +- Exercise +- Variability +- Unimodal Distribution of Data +- Bimodal Distribution of Data +- Normal distribution – Part 1 +- Normal distribution – Part 2 +- Z-Score +- Introduction to Probability- An Overview +- Principal Of Counting +- Exercise +- Permutation +- Exercise +- Combination +- Exercise +- Conditional Probability – Part 1 +- Conditional Probability – Part 2 +- Exercise +- Binomial Distribution +- Random variable +- Expectation and variance +- Exercise +- Cheatsheet for Probability +- Statistics: Inferential-Hypothesis Testing +- T-test +- One Way ANOVA +- Chi-square +- Cheatsheet on Statistics +- Exploratory Data Analysis (EDA)- Data Exploration +- Cheatsheet on EDA +- Project-1 | Loan Prediction +- Project-2 | Big Mart Sales +- Linear Algebra +- Free Course +April 2024: Basics of Machine Learning and art of storytelling +- Overview of Machine Learning +- Understanding Data Science Pipeline +- Get Familiarised with Command Line (Linux)- Guide +- Linear Regression +- Linear Regression-Video +- Exercise +- Logistic Regression- Part 1 +- Logistic Regression – Part 2 +- Exercise +- Decision Tree Algorithm +- Exercise +- Naive Bayes +- Support Vector Machine +- Regression Project - Big Mart Sales +- Classification Project - Loan Prediction +- Introduction to Structured Thinking +- Commonly Asked Puzzles in Interviews +- How to solve Guesstimates? +- Excercise: Strategic Thinking +- Structured Thinking and Communication Course +May 2024: Advanced Machine Learning +- Ensemble Learning Basics +- Ensemble Learning Basics-Video +- Bagging +- Boosting +- Random Forest - Simplified +- Random Forest - Detailed with implementation +- Exercise +- Boosting - Detailed with implementation +- XGBoost +- LightGBM +- CatBoost +- Exercise +- Advanced Ensemble Technique - Blending +- Advanced Ensemble Learning - Stacking +- Cheatsheet for Machine Learning +- Image data +- Text data +- Audio data +- Audio data-Video +- Projects +- Participating in Competitions +- Introduction to validation +- Different Types of Validation Techniques +- K-fold Cross Validation - Implementation +- Summary - Validation Techniques +- Exercise +- Different methods for finding best hyperparameters of an algorithm +- Hyperparameter tuning for Random Forest +- Hyperparameter tuning for GBM +- Hyperparameter tuning for XGBoost +- Hyperparameter tuning for LightGBM +- Bayesian Hyperparameter Optimization +- Feature Engineering +- Profile Building +- Building your Resume +- Up Level your Data Science Resume Course +- Ace Data Science Interview Course +June 2024: Other Machine Learning Concepts +- Basics of Matrix Algebra +- Matrix Calculus +- Dimensionality Reduction - Overview +- Principal Component Analysis (PCA) +- Singular Value Decomposition (SVD) +- Singular Value Decomposition (SVD)-Text +- Unsupervised Learning-K Means and Hierarchical Clustering +- Clustering - Project +- Learn Github +- Introduction to Recommendation Systems +- Introduction to Recommendation Systems - Video +- Project: Recommendation System +- Implementation in Python +- Introduction to Time Series Forecasting +- Handling a Non-Stationary Time Series in Python +- Time Series Modeling using ARIMA +- Time Series Modeling using Prophet Library +- Time Series Project +- Project - Black Friday +July 2024: Introduction to Deep Learning and Computer Vision +- Setting up the System for Deep Learning +- Introduction to Deep Learning +- Build your first Neural Network in Numpy +- Why are GPUs necessary for Deep Learning? +- The Evolution and Core Concepts of Deep Learning & Neural Networks +- An Introduction to Implementing Neural Networks using TensorFlow +- Introduction to Keras +- Optimizing Neural Networks using Keras (with Image recognition case study) +- Understanding Convolutional Neural Networks (CNNs) +- Build Image Classification Model using Keras +- Exercise +- Transfer Learning for Computer Vision +- Computer Vision Project 1 : Identify the Apparels +- Computer Vision Project 2: Scene Classification +- Computer Vision using Deep Learning Course +- Computer Vision Course (Sponsored) +- Cheatsheet for Keras +- Write for Analytics Vidhya's Medium Publication +August 2024: Basics of Natural Language Processing +- Recurrent Neural Network +- Long short Term Memory Networks (LSTM) +- Gated Recurrent Unit (GRU) +- Useful resources-GRU +- Text Preprocessing +- Text Cleaning +- Text Classification +- Natural Language Processing (NLP) Using Python Course +September 2024: Model Deployment +- How to Deploy Machine Learning Models using Flask +- Tutorial to deploy Machine Learning models in Production as APIs +- Deploying machine learning models using Streamlit – An introductory guide to Model Deployment +- An Ode to Model Deployment using Streamlit – Open Sourcing “Typing Tutor for Programmers” +October 2024: Practice and Projects +- Building a Portfolio with Projects","About the courseWhere do I begin? Data science is such a huge field - where do you even start learning about Data Science?These are career-defining questions often asked by data science aspirants. There are a million resources out there to refer but the learning journey can be quite exhausting if you don’t know where to start.Don’t worry, we are here to help you take your first steps into the world of data science! Here’s the learning path for people who want to become a data scientist in 2023. We have arranged and compiled all the best resources in a structured manner so that you have a unified resource to become a successful data scientist.Moreover, we have added the most in-demand skills for the year 2023 for data scientists including storytelling, model deployment, and much more along with exercises and assignments." +Nano Course: Building Large Language Models for Code,"Building Large Language Models for Code +- Introduction +- Agenda +- BigCode Community +- Training LLMs for Code from Scratch: Training Data Curation +- Training Data Formatting and Preprocessing +- Model Architecture +- BigCode Ecosystem +- Training Frameworks +- Model Evaluation +- Tools and Descendants of StarCoder","Nano Course: Building Large Language Models for CodeIn this Free Nano GenAI Course onBuilding Large Language Models for Code, you will-Learn how to train LLMs for Code from Scratch covering Training Data Curation, Data Preparation, Model Architecture, Training, and Evaluation Frameworks.Explore each step in-depth, delving into the algorithms and techniques used to create StarCoder, a 15B code generation model trained on 80+ programming languages.Understand and learn the best practices to train your own StarCoder on the data" +Title not found,Curriculum not found,An error occurred while fetching the page: 404 Client Error: Not Found for url: https://courses.analyticsvidhya.com/courses/certified-ai-ml-blackbelt-plus +Machine Learning Summer Training,"Overview of the Course +- AI&ML Blackbelt Plus Program (Sponsored) +Introduction to Data Science and Machine Learning +- Overview of Machine Learning / Data Science +- Common Terminology used in Data Science +- Applications of Data Science +Setting up your system +- Installation steps for Windows +- Installation steps for Linux +- Installation steps for Mac +Introduction to Python +- Introduction to Python +- Introduction to Jupyter Notebook +- Download Python Module Handouts +Variables and Data Types +- Introduction to Variables +- Implementing Variables in Python +Operators +- Introduction to Operators +- Implementing Operators in Python +- Quiz: Operators +Conditional Statements +- Introduction to Conditional Statements +- Implementing Conditional Statements in Python +- Quiz: Conditional Statements +Looping Constructs +- Introduction to Looping Constructs +- Implementing Loops in Python +- Quiz: Loops in Python +- Break, Continue and Pass Statements +- Quiz: Break, Continue and Pass Statement +Data Structures +- Introduction to Data Structures +- List and Tuple +- Implementing List in Pyhton +- Quiz: Lists +- List - Project in Python +- Implementing Tuple in Python +- Quiz: Tuple +- Introduction to Sets +- Implementing Sets in Python +- Quiz: Sets +- Introduction to Dictionary +- Implementing Dictionary in Python +- Quiz: Dictionary +String Manipulation +- Introduction to String Manipulation +- Quiz: String Manipulation +Functions +- Introduction to Functions +- Implementing Functions in Python +- Quiz: Functions in Python +- Lambda Expression +- Quiz: Lambda Expressions +- Recursion +- Implementing Recursion in Python +- Quiz: Recursion +Modules, Packages and Standard Libraries +- Introduction to Modules +- Modules: Intuition +- Introduction to Packages +- Standard Libraries in Python +- User Defined Libraries in Python +- Quiz: Modules, Packages and Standard Libraries +Handling Text Files in Python +- Handling Text Files in Python +- Quiz: Handling Text Files +Introduction to Python Libraries for Data Science +- Important Libraries for Data Science +- Quiz: Important Libraries for Data Science +Python Libraries for Data Science +- Basics of Numpy in Python +- Basics of Scipy in Python +- Quiz: Numpy and Scipy +- Basics of Pandas in Python +- Quiz: Pandas +- Basics of Matplotlib in Python +- Basics of Scikit-Learn in Python +- Basics of Statsmodels in Python +Reading Data Files in Python +- Reading Data in Python +- Reading CSV files in Python +- Reading Big CSV Files in Python +- Quiz: Reading CSV files in Python +- Reading Excel & Spreadsheet files in Python +- Quiz: Reading Excel & Spreadsheet files in Python +- Reading JSON files in Python +- Quiz: Reading JSON files in Python +Preprocessing, Subsetting and Modifying Pandas Dataframes +- Subsetting and Modifying Data in Python +- Overview of Subsetting in Pandas I +- Overview of Subsetting in Pandas II +- Subsetting based on Position +- Subsetting based on Label +- Subsetting based on Value +- Quiz: Subsetting Dataframes +- Modifying data in Pandas +- Quiz: Modifying Dataframes +Sorting and Aggregating Data in Pandas +- Preprocessing, Sorting and Aggregating Data +- Sorting the Dataframe +- Quiz: Sorting Dataframes +- Concatenating Dataframes in Pandas +- Concept of SQL-Like Joins in Pandas +- Implementing SQL-Like Joins in Pandas +- Quiz: Joins in Pandas +- Aggregating and Summarizing Dataframes +- Preprocessing Timeseries Data +- Quiz: Preprocessing Timeseries Data +Visualizing Patterns and Trends in Data +- Visualizing Trends & Pattern in Data +- Basics of Matplotlib +- Data Visualization with Matplotlib +- Quiz: Matplotlib +- Basics of Seaborn +- Data Visualization with Seaborn +- Quiz: Seaborn +Machine Learning Lifecycle +- 6 Steps of Machine Learning Lifecycle +- Introduction to Predictive Modeling +Problem statement and Hypothesis Generation +- Defining the Problem statement +- Introduction to Hypothesis Generation +- Performing Hypothesis generation +- Quiz - Performing Hypothesis generation +- List of hypothesis +- Data Collection/Extraction +- Quiz - Data Collection/Extraction +Importance of Stats and EDA +- Introduction to Exploratory Data Analysis & Data Insights +- Quiz - Introduction to Exploratory Data Analysis & Data Insights +- Role of Statistics in EDA +- Descriptive Statistics +- Inferential Statistics +- Quiz - Descriptive and Inferential Statistics +Build Your First Predictive Model +- Quiz: Build a Benchmark Model - Regression +- Benchmark Model: Regression Implementation +- Quiz: Benchmark Model - Regression Implementation +- Build a Benchmark Model: Classification +- Quiz: Build a Benchmark Model - Classification +- Benchmark Model: Classification Implementation +- Quiz: Benchmark - Classification Implementation +Evaluation Metrics +- Introduction to Evaluation Metrics +- Quiz: Introduction to Evaluation Metrics +- Confusion Matrix +- Quiz: Confusion Matrix +- Accuracy +- Quiz: Accuracy +- Alternatives of Accuracy +- Quiz: Alternatives of Accuracy +- Precision and Recall +- Quiz: Precision and Recall +- Thresholding +- Quiz: Thresholding +- AUC-ROC +- Quiz: AUC-ROC +- Log loss +- Quiz: Log loss +- Evaluation Metrics for Regression +- Quiz: Evaluation Metrics for Regression +- R2 and Adjusted R2 +- Quiz: R2 and Adjusted R2 +Preprocessing Data +- Dealing with Missing Values in the Data +- Quiz: Dealing with missing values in the data +- Replacing Missing Values +- Quiz: Replacing Missing values +- Imputing Missing Values in data +- Quiz: Imputing Missing values in data +- Working with Categorical Variables +- Quiz: Working with categorical data +- Working with Outliers +- Quiz: Working with outliers +- Preprocessing Data for Model Building +Build Your First ML Model: k-NN +- Building a kNN model +- Quiz: Building a kNN model +- Determining right value of k +- Quiz: Determining right value of k +- How to calculate the distance +- Quiz: How to calculate the distance +- Issue with distance based algorithms +- Quiz: Issue with distance based algorithms +- Introduction to sklearn +- Implementing k-Nearest Neighbours algorithm +- Quiz: Implementing k-Nearest Neighbours algorithm +Selecting the Right Model +- Introduction to Overfitting and Underfitting Models +- Quiz: Introduction to Overfitting and Underfitting Models +- Visualizing overfitting and underfitting using knn +- Quiz: Visualizing overfitting and underfitting using knn +- Selecting the Right Model +- What is Validation? +- Quiz: What is Validation +- Understanding Hold-Out Validation +- Quiz: Understanding Hold-Out Validation +- Implementing Hold-Out Validation +- Quiz: Implementing Hold-Out Validation +- Understanding k-fold Cross Validation +- Implementing k-fold Cross Validation +- Quiz: Understanding k-fold Cross Validation +- Quiz: Implementing k-fold Cross Validation +- Bias Variance Tradeoff +- Quiz: Bias Variance Tradeoff +Linear Models +- Introduction to Linear Models +- Quiz: Introduction to linear model +- Understanding Cost function +- Quiz: Understanding Cost function +- Understanding Gradient descent (Intuition) +- Maths behind gradient descent +- Convexity of cost function +- Quiz: Convexity of Cost function +- Quiz: Gradient Descent +- Assumptions of Linear Regression +- Quiz: Assumptions of linear model +- Implementing Linear Regression +- Generalized Linear Models +- Quiz: Generalized Linear Models +- Introduction to Logistic Regression +- Quiz: Introduction to logistic regression +- Quiz: Logistic Regression +- Odds Ratio +- Implementing Logistic Regression +- Multiclass using Logistic Regression +- Quiz: Multi-Class Logistic Regression +- Challenges with Linear Regression +- Quiz: Challenges with Linear regression +- Introduction to Regularisation +- Quiz: Introduction to Regularization +- Implementing Regularisation +- Coefficient estimate for ridge and lasso (Optional) +Project: Customer Churn Prediction +- Predicting whether a customer will churn or not +Decision Tree +- Introduction to Decision Trees +- Quiz: Introduction to Decision Trees +- Purity in Decision Trees +- Quiz: Purity in Decision Trees +- Terminologies Related to Decision Trees +- Quiz: Terminologies Related to Decision Trees +- How to Select the Best Split Point in Decision Trees +- Quiz: How to Select the Best Split Point in Decision Trees +- Chi-Square +- Quiz: Chi-Square +- Information Gain +- Quiz: Information Gain +- Reduction in Variance +- Quiz: Reduction in Variance +- Optimizing Performance of Decision Trees +- Quiz: Optimizing Performance of Decision Trees +- Decision Tree Implementation +Feature Engineering +- Introduction to Feature Engineering +- Quiz: Introduction to feature engineering +- Exercise on Feature Engineering +- Overview of the module +- Feature Transformation +- Quiz: Feature Transformation +- Feature Scaling +- Quiz: Feature Scaling +- Feature Encoding +- Quiz: Feature Encoding +- Combining Sparse classes +- Quiz: Combining Sparse classes +- Feature Generation: Binning +- Quiz: Feature Generation- Binning +- Feature Interaction +- Quiz: Feature Interaction +- Generating Features: Missing Values +- Frequency Encoding +- Quiz: Frequency Encoding +- Feature Engineering: Date Time Features +- Implementing DateTime Features +- Quiz: Implementing DateTime Features +- Automated Feature Engineering : Feature Tools +- Implementing Feature tools +- Quiz: Implementing Feature Tools +Project: NYC Taxi Trip Duration prediction +- Exploring the NYC dataset +- Predicting the NYC taxi trip duration +- Predicting the NYC taxi trip duration +Feedback +- Share Your Feedback about the course. +- Would you recommend this course to your Friends.","This is the second step of theMachine Learning Summer Training, want to know moreclick here." +AI Ethics by Fractal,"AI Ethics +- Fractal's Ethical AI Principles +- Framework: Behaviors and toolkits overview +- Next Steps & Further Learning +- Test Your Self","Key purposes of the Training on AI EthicsAI has a huge influence on our lives. From typing on our smartphones, to personalized recommendations on our favourite shopping websites, intelligent machines are everywhere. Our interactions with technology have become more personalized, but with humans ultimately behind these creations, the question is: where does the responsibility lie? Why and how should we begin the AI ethics conversation at Fractal?Learning plan:The video course is followed byMCQtest to gauge the depth of your understanding and help you retain your learning.Learners can take the e-learning and complete theMCQTest activity post viewing the video." +A Comprehensive Learning Path to Become a Data Engineer in 2022,"Overview of Learning Path 2022 +- Overview of Learning Path +- Month-on-Month Plan +- AI&ML Blackbelt Plus Program (Sponsored) +January 2022: Learn Programming +- Overview of the Course +- A brief introduction to Python +- Introduction to Python Test +- Installing Python +- Become a BlackBelt in Data Science +- Theory of Operators +- Exercise +- Understanding Operators in Python +- Operators Test +- Understanding variables and data types +- Variable Test +- Variables and Data Types in Python +- Exercise +- Understanding Conditional Statements +- Exercise +- Implementing Conditional Statements in Python +- Conditional Statements test +- Understanding Looping Constructs +- Exercise +- Implementing Looping Constructs in Python +- Looping Constructs test +- Understanding Functions +- Implementing Functions in Python +- Functions test +- A brief introduction to data structure +- Data Structure test +- Understanding the concept of Lists +- Lists test +- Implementing Lists in Python +- Exercise +- Understanding the concept of Dictionaries +- Exercise +- Implementing Dictionaries in Python +- Dictionaries test +- Understanding the concept of Standard Libraries +- Libraries test +- Reading a CSV File in Python - Introduction to Pandas +- Reading a CSV file in Python: Implementation +- Reading a csv file in Python test +- Understanding dataframes and basic operations +- DataFrames and basic operations test +- Reading dataframes and conduct basic operations in Python +- Reading dataframes and conduct basic operations in Python Test +- Indexing a Dataframe +- Indexing DataFrames test +- Exercise +- Sorting Dataframes +- Merging Dataframes +- Quiz: Sorting and Merging dataframes +- Apply function +- Aggregating data +- Quiz: Apply function and Aggregating data +- Basics of Matplotlib +- Data Visualization using Matplotlib +- Quiz: Matplotlib +- Basics of Seaborn +- Data Visualization using Seaborn +- Quiz: Seaborn +- Regular Expressions +- Understanding Regular Expressions +- Quiz: Regular Expressions +- Regular Expressions in Python +- Quiz: Regular Expressions in Python +- Cheatsheet for Python +- Instructions +- Quiz +- Python Coding Challenge +- Test your Skills: Python +- Poll +- Where to go from here? +February 2022: Learn Relational Databases +- Plan for February 2022 +- 1.5 Types of Databases +- 1.6 How data is Stored in Relational Databases +- 1.7 How data is stored in NoSQL databases +- Exercise 1 +- Course Handouts +- 2.1 Introduction +- 2.2 Architecture: Client and Server +- 2.3 MySQL Distributions +- 2.4 Local Installation on Mac +- 2.5 Local Installation on Linux +- 2.6 Local Installation on Windows +- 2.7 Licensing +- 2.8 Accessing a remote MySQL server +- 2.9 Graphical user interfaces +- Exercise 2 +- SQL - Installation Guide +- 3.1 Introduction +- 3.2 What exactly is SQL? +- 3.3 History of SQL +- 3.4 Connecting to MySQL +- 3.5 Types of Commands - DDL (Creation/ Deletion/ Updating of Schema +- 3.6 Types of Commands - DML (Manipulating data in tables) +- 3.7 Types of Commands - DCL (Managing Access control) +- 3.8 Exploring databases +- 3.9 Creating tables +- 3.10 Inserting data in tables +- 3.11 SELECT Statement - Introduction +- 3.12 Datatypes in MySQL +- 3.13 NULL vs NOT NULL +- Exercise 3 +- 4.1 Introduction +- 4.2 Update command – Concept +- 4.3 Update command – Example +- 4.4 Delete command – Concept +- 4.5 Delete command – Example +- 4.6 Describe command – Concept +- 4.7 Describe command – Example +- 4.8 Alter command – Concept and Example +- Copy of Exercise 4 +- 5.1 Introduction +- 5.2 Importing data from CSV to MySQL +- 5.3 Exporting data from MySQL to CSV +- 5.4 Backing up databases +- 5.5 Restoring databases +- Exercise 5 +- Importing and Exporting Datasets - Troubleshooting Guide +- 6.1 Introduction +- 6.2 Counting Rows and Items +- 6.3 Aggregation Functions – SUM, AVG, STDDEV +- 6.4 Extreme Values Identification – MIN, MAX +- 6.5 Slicing data +- 6.6 Limiting data +- 6.7 Sorting data +- 6.8 Filtering Patterns +- 6.9 Groupings, Rolling up data and Filtering in Groups +- Exercise 6 +- 7.1 Introduction +- 7.2 Data Eyeballing +- 7.3 Data Dictionary +- 7.4 Questions we need answers of +- 7.5 Analyzing data and creating table structure +- 7.6 Loading data to our MySQL table +- 7.7 Data Analysis – Simple Queries +- 7.8 Data Analysis – Advanced Queries +- FIFA19 Players dataset (cleaned) for this Project +- 8.1 Introduction +- 8.2. The need for joins +- 8.3. Different type of joins +- 8.4. The Left Join - Concept +- 8.5. The Left Join – Practical Example +- 8.6. The Inner Join +- 8.7. The Cross Join +- 8.8. The Right Join +- 8.9. The Self Join +- Assignment: Share your learning and build your profile +- Exercise +- 9.1. Introduction +- 9.2. Introduction to Indexing +- 9.3. How indexing works (basics) +- 9.4. Relationships +- 9.5. Types of Relationships +- 9.6. Table Constraints – PRIMARY KEY, FOREIGN KEY, UNIQUENESS and AUTO INCREMENT +- Exercise +- 10.1 String functions - CONCAT +- 10.2 String functions – Case Conversion +- 10.3 String functions – Trimming Strings +- 10.4 String functions – Extracting Substrings +- 10.5 Date/ Time functions – Current date and time +- 10.6 Date/ Time functions – Extracting date and time from field +- 10.7 Date/ Time functions – Formatting date and time as Strings +- 10.8 Numeric functions +- SQL CheatSheet +- Exercise +- 11.1 Introduction +- 11.2 Setting up a virtual environment +- 11.3 Installing the required packages +- 11.4 Connecting to MySQL +- 11.5 Connecting to database table and pulling data +- 11.6 Querying the database- INSERT +- 11.7 Querying the database- DELETE +- 11.8 Querying the database- SEARCH +- 11.9 Querying the database- INDEXING +- 11.10 Notes and Resources +- Exercise +March 2022: Fundamentals of Linux and Cloud Computing +- Basic Linux Commands +- Introduction to Cloud Computing +- Cloud Deployment Models +- Service Models +- Resources: Learn about AWS +April 2022 : NoSQL Databases +- Creating Databases and Collections +- Inserting Documents +- Reading Documents +- The _id Field +- Importing and Exporting Data +- Backup and Restore MongoDB Databases +- Updating Documents +- Deleting Documents, Collections and Databases +- CRUD Operations in MongoDB Atlas +- Importing, Exporting and Working with MongoDB Atlas +May 2022: Hadoop Ecosystem +- What is Big Data? +- Challenges with Big Data +- Applications of Big Data +- Distributed Systems +June 2022: Data Warehousing +- What is Hive +- Features of Hive +- Working of Hive","About the courseWhere do I begin? Data Engineering is such a huge field - where do you even start learning about Data Engineering?These are career-defining questions often asked by data engineering aspirants. There are a million resources out there to refer but the learning journey can be quite exhausting if you don’t know where to start.Don’t worry, we are here to help you take your first steps into the world of data engineering! Here’s the learning path for people who want to become a data engineer in 2022. We have arranged and compiled all the best resources in a structured manner so that you have a unified resource to become a successful data engineer.Moreover, we have added the most in-demand skills for the year 2022 for data engineers including storytelling, model deployment, and much more along with exercises and assignments." +Title not found,Curriculum not found,An error occurred while fetching the page: 404 Client Error: Not Found for url: https://courses.analyticsvidhya.com/courses/certified-business-analytics-program +Title not found,Curriculum not found,An error occurred while fetching the page: 404 Client Error: Not Found for url: https://courses.analyticsvidhya.com/courses/certified-machine-learning-master-s-program-mlmp +Title not found,Curriculum not found,An error occurred while fetching the page: 404 Client Error: Not Found for url: https://courses.analyticsvidhya.com/courses/certified-natural-language-processing-master-s-program +Title not found,Curriculum not found,An error occurred while fetching the page: 404 Client Error: Not Found for url: https://courses.analyticsvidhya.com/courses/certified-computer-vision-masters-program +Applied Machine Learning - Beginner to Professional,"Introduction to Data Science and Machine Learning +Introduction to the Course +- Course Handouts +Setting up your system +- Installation steps for Windows +- Installation steps for Linux +- Installation steps for Mac +Introduction to Python +- Introduction to Python +- Introduction to Jupyter Notebook +- Download Python Module Handouts +Variables and Data Types +- Introduction to Variables +- Implementing Variables in Python +Operators +- Introduction to Operators +- Implementing Operators in Python +- Quiz: Operators +Conditional Statements +- Introduction to Conditional Statements +- Implementing Conditional Statements in Python +- Quiz: Conditional Statements +Looping Constructs +- Introduction to Looping Constructs +- Implementing Loops in Python +- Quiz: Loops in Python +- Break, Continue and Pass Statements +- Quiz: Break, Continue and Pass Statement +Data Structures +- Introduction to Data Structures +- List and Tuple +- Implementing List in Python +- Quiz: Lists +- List - Project in Python +- Implementing Tuple in Python +- Quiz: Tuple +- Introduction to Sets +- Implementing Sets in Python +- Quiz: Sets +- Introduction to Dictionary +- Implementing Dictionary in Python +- Quiz: Dictionary +- Assignment: Data Structures +- Project: Personal Expense tracker +String Manipulation +- Introduction to String Manipulation +- Quiz: String Manipulation +Functions +- Introduction to Functions +- Implementing Functions in Python +- Quiz: Functions in Python +- Lambda Expression +- Quiz: Lambda Expressions +- Recursion +- Implementing Recursion in Python +- Quiz: Recursion +- Expert talk: Rajiv Shah +- Project: Hangman +Modules, Packages and Standard Libraries +- Introduction to Modules +- Modules: Intuition +- Introduction to Packages +- Standard Libraries in Python +- User Defined Libraries in Python +- Quiz: Modules, Packages and Standard Libraries +Handling Text Files in Python +- Handling Text Files in Python +- Quiz: Handling Text Files +Introduction to Python Libraries for Data Science +- Important Libraries for Data Science +- Quiz: Important Libraries for Data Science +Python Libraries for Data Science +- Basics of Numpy in Python +- Basics of Scipy in Python +- Quiz: Numpy and Scipy +- Basics of Pandas in Python +- Quiz: Pandas +- Basics of Matplotlib in Python +- Basics of Scikit-Learn in Python +- Basics of Statsmodels in Python +- Unlock the Data Science Universe with Andrew Engel: Insights, Innovations, and Beyond! +Reading Data Files in Python +- Reading Data in Python +- Reading CSV files in Python +- Reading Big CSV Files in Python +- Quiz: Reading CSV files in Python +- Reading Excel & Spreadsheet files in Python +- Quiz: Reading Excel & Spreadsheet files in Python +- Reading JSON files in Python +- Quiz: Reading JSON files in Python +- Assignment: Reading Data Files in Python +Preprocessing, Subsetting and Modifying Pandas Dataframes +- Subsetting and Modifying Data in Python +- Overview of Subsetting in Pandas I +- Overview of Subsetting in Pandas II +- Subsetting based on Position +- Subsetting based on Label +- Subsetting based on Value +- Quiz: Subsetting Dataframes +- Modifying data in Pandas +- Quiz: Modifying Dataframes +- Assignment: Subsetting and Modifying Pandas Dataframes +Sorting and Aggregating Data in Pandas +- Preprocessing, Sorting and Aggregating Data +- Sorting the Dataframe +- Quiz: Sorting Dataframes +- Concatenating Dataframes in Pandas +- Concept of SQL-Like Joins in Pandas +- Implementing SQL-Like Joins in Pandas +- Quiz: Joins in Pandas +- Aggregating and Summarizing Dataframes +- Preprocessing Timeseries Data +- Quiz: Preprocessing Timeseries Data +- Assignment: Sorting and Aggregating Data in Pandas +Visualizing Patterns and Trends in Data +- Visualizing Trends & Pattern in Data +- Basics of Matplotlib +- Data Visualization with Matplotlib +- Quiz: Matplotlib +- Basics of Seaborn +- Data Visualization with Seaborn +- Quiz: Seaborn +- Assignment: Visualizing Patterns and Trends in Data +Machine Learning Lifecycle +- 6 Steps of Machine Learning Lifecycle +- Introduction to Predictive Modeling +Problem statement and Hypothesis Generation +- Defining the Problem statement +- Introduction to Hypothesis Generation +- Performing Hypothesis generation +- Quiz - Performing Hypothesis generation +- List of hypothesis +- Data Collection/Extraction +- Quiz - Data Collection/Extraction +Importance of Stats and EDA +- Introduction to Exploratory Data Analysis & Data Insights +- Quiz - Introduction to Exploratory Data Analysis & Data Insights +- Role of Statistics in EDA +- Descriptive Statistics +- Inferential Statistics +- Quiz - Descriptive and Inferential Statistics +Understanding Data +- Introduction to dataset +- Quiz - Introduction to dataset +- Reading data files into python +- Quiz - Reading data files into python +- Different Variable Datatypes +- Variable Identification +- Quiz - Variable Identification +Probability +- Probability for Data Science +- Quiz - Probability for Data Science +- Basic Concepts of Probability +- Quiz - Basic Concepts of Probability +- Axioms of Probability +- Quiz - Axioms of Probability +- Conditional Probability +- Quiz - Conditional Probability +Exploring Continuous Variable +- Data range for continuous variables +- Central Tendencies for continuous variables +- Spread of the data +- Central Tendencies and Spread of the data: Implementation +- Quiz: Central Tendencies and Spread of data +- KDE plots for continuous variable +- KDE plots : Implementation +- Overview of Distributions for Continuous Variables +- Normal Distribution +- Normality Check +- Skewed Distribution +- Skewness and Kurtosis +- Distributions for continuous variable +- Quiz: Distribution of Continuous variables +- Approaching Univariate Analysis +- Approaching Univariate Analysis: Numerical Variables +- Quiz: Univariate analysis for Continuous variables +Exploring Categorical Variables +- Central Tendencies for categorical variables +- Understanding Discrete Distributions +- Discrete Distributions Demonstration +- Performing EDA on Catagorical Variables +- Quiz: Univariate Analysis for Categorical Variables +Missing Values and Outliers +- Dealing with Missing values +- Understanding Outliers +- Identifying Outliers in data +- Identifying Outliers in data: Implementation +- Quiz: Identifying Outliers in datasets +- Quiz: Outlier treatment +Central Limit theorem +- Important Terminologies +- Central Limit Theorem +- CLT: Implementation +- Quiz: Central Limit Theorem +- Confidence Interval and Margin of error +Bivariate analysis - Introduction +- Introduction to Bivariate Analysis +Continuous - Continuous Variables +- Covariance +- Pearson Correlation +- Spearman's Correlation & Kendall's Tau +- Correlation versus Causation +- Tabular and Graphical Methods +- Performing Bivariate Analysis on Continuous - Continuous variables +- Quiz: Continuous-Continuous Variables +Continuous Categorical +- Tabular and Graphical Methods +- Introduction to hypothesis Testing +- P-Value +- One Sample z-test +- Two Sampled z-test +- Quiz: Hypothesis Testing and Z scores +- T-Test +- T-Test vs Z-Test +- Quiz: T tests +- Performing Bivariate Analysis on Catagorical - Continuous variables +Categorical Categorical +- Tabular and Graphical Methods +- Chi-Squared Test +- Quiz: Chi squared tests +- Bivariate Analysis for Categorical Categorical Variables +Multivariate Analysis +- Multivariate Analysis +- Multivariate Analysis Implementation +- Project: EDA +Assignments +- Understanding the NYC Taxi Trip Duration Problem +- Assignment: EDA +Build Your First Predictive Model +- Quiz: Build a Benchmark Model - Regression +- Benchmark Model: Regression Implementation +- Quiz: Benchmark Model - Regression Implementation +- Build a Benchmark Model: Classification +- Quiz: Build a Benchmark Model - Classification +- Benchmark Model: Classification Implementation +- Quiz: Benchmark - Classification Implementation +Evaluation Metrics +- Introduction to Evaluation Metrics +- Quiz: Introduction to Evaluation Metrics +- Confusion Matrix +- Quiz: Confusion Matrix +- Accuracy +- Quiz: Accuracy +- Alternatives of Accuracy +- Quiz: Alternatives of Accuracy +- Precision and Recall +- Quiz: Precision and Recall +- Thresholding +- Quiz: Thresholding +- AUC-ROC +- Quiz: AUC-ROC +- Log loss +- Quiz: Log loss +- Evaluation Metrics for Regression +- Quiz: Evaluation Metrics for Regression +- R2 and Adjusted R2 +- Quiz: R2 and Adjusted R2 +Preprocessing Data +- Dealing with Missing Values in the Data +- Quiz: Dealing with missing values in the data +- Replacing Missing Values +- Quiz: Replacing Missing values +- Imputing Missing Values in data +- Quiz: Imputing Missing values in data +- Working with Categorical Variables +- Quiz: Working with categorical data +- Working with Outliers +- Quiz: Working with outliers +- Preprocessing Data for Model Building +Build Your First ML Model: k-NN +- Building a kNN model +- Quiz: Building a kNN model +- Determining right value of k +- Quiz: Determining right value of k +- How to calculate the distance +- Quiz: How to calculate the distance +- Issue with distance based algorithms +- Quiz: Issue with distance based algorithms +- Introduction to sklearn +- Implementing k-Nearest Neighbours algorithm +- Quiz: Implementing k-Nearest Neighbours algorithm +Selecting the Right Model +- Introduction to Overfitting and Underfitting Models +- Quiz: Introduction to Overfitting and Underfitting Models +- Visualizing overfitting and underfitting using knn +- Quiz: Visualizing overfitting and underfitting using knn +- Selecting the Right Model +- What is Validation? +- Quiz: What is Validation +- Understanding Hold-Out Validation +- Quiz: Understanding Hold-Out Validation +- Implementing Hold-Out Validation +- Quiz: Implementing Hold-Out Validation +- Understanding k-fold Cross Validation +- Implementing k-fold Cross Validation +- Quiz: Understanding k-fold Cross Validation +- Quiz: Implementing k-fold Cross Validation +- Bias Variance Tradeoff +- Quiz: Bias Variance Tradeoff +Linear Models +- Introduction to Linear Models +- Quiz: Introduction to linear model +- Understanding Cost function +- Quiz: Understanding Cost function +- Understanding Gradient descent (Intuition) +- Maths behind gradient descent +- Convexity of cost function +- Quiz: Convexity of Cost function +- Quiz: Gradient Descent +- Assumptions of Linear Regression +- Quiz: Assumptions of linear model +- Implementing Linear Regression +- Download: Implementing Linear Regression +- Generalized Linear Models +- Quiz: Generalized Linear Models +- Introduction to Logistic Regression +- Quiz: Introduction to logistic regression +- Quiz: Logistic Regression +- Odds Ratio +- Implementing Logistic Regression +- Multiclass using Logistic Regression +- Quiz: Multi-Class Logistic Regression +- Challenges with Linear Regression +- Quiz: Challenges with Linear regression +- Introduction to Regularisation +- Quiz: Introduction to Regularization +- Implementing Regularisation +- Coefficient estimate for ridge and lasso (Optional) +- Expert Talk: Vikas Kumrawat +Project: Customer Churn Prediction +- Predicting whether a customer will churn or not +- Assignment: NYC taxi trip duration prediction +Dimensionality Reduction (Part I) +- Introduction to Dimensionality Reduction +- Quiz: Introduction to Dimensionality Reduction +- Common Dimensionality Reduction Techniques +- Quiz: Common Dimensionality Reduction Techniques +- Missing Value Ratio +- Missing Value Ratio Implementation +- Quiz: Missing Value Ratio +- Low Variance Filter +- Low Variance Filter Implementation +- Quiz: Low Variance Filter +- High Correlation Filter +- High Correlation Filter Implementation +- Quiz: High Correlation Filter +- Backward Feature Elimination +- Backward Feature Elimination Implementation +- Quiz: Backward Feature Elimination +- Forward Feature Selection +- Forward Feature Selection Implementation +- Quiz: Forward Feature Selection +Decision Tree +- Introduction to Decision Trees +- Quiz: Introduction to Decision Trees +- Purity in Decision Trees +- Quiz: Purity in Decision Trees +- Terminologies Related to Decision Trees +- Quiz: Terminologies Related to Decision Trees +- How to Select the Best Split Point in Decision Trees +- Quiz: How to Select the Best Split Point in Decision Trees +- Chi-Square +- Quiz: Chi-Square +- Information Gain +- Quiz: Information Gain +- Reduction in Variance +- Quiz: Reduction in Variance +- Optimizing Performance of Decision Trees +- Quiz: Optimizing Performance of Decision Trees +- Decision Tree Implementation +- Expert Talk: Vijoe Mathew on Mastering Data Science: Insights, Strategies and Career Tips +Feature Engineering +- Introduction to Feature Engineering +- Quiz: Introduction to feature engineering +- Exercise on Feature Engineering +- Overview of the module +- Feature Transformation +- Quiz: Feature Transformation +- Feature Scaling +- Quiz: Feature Scaling +- Expert Talk: Jaidev Deshpande +- Feature Encoding +- Quiz: Feature Encoding +- Combining Sparse classes +- Quiz: Combining Sparse classes +- Feature Generation: Binning +- Quiz: Feature Generation- Binning +- Feature Interaction +- Quiz: Feature Interaction +- Generating Features: Missing Values +- Frequency Encoding +- Quiz: Frequency Encoding +- Feature Engineering: Date Time Features +- Implementing DateTime Features +- Quiz: Implementing DateTime Features +- Automated Feature Engineering : Feature Tools +- Implementing Feature tools +- Quiz: Implementing Feature Tools +- Expert talk: Sudalai Rajkumar +Share your Learnings +- Write for Analytics Vidhya's Medium Publication +Project: NYC Taxi Trip Duration prediction +- Exploring the NYC dataset +- Predicting the NYC taxi trip duration +- Predicting the NYC taxi trip duration +Working with Text Data +- Introduction to Text Feature Engineering +- Quiz: Introduction to Text Feature Engineering +- Create Basic Text Features +- Quiz: Create Basic Text Features +- Extract Information using Regular Expressions +- Quiz: Extract Information using Regular Expressions +- Learn to use Regular Expressions in Python +- Quiz: Learn to use Regular Expressions in Python +- Text Cleaning +- Quiz: Text Cleaning +- Create Linguistic Features +- Quiz: Create Linguistic Features +- Bag-of-Words +- Quiz: Bag-of-Words +- Text Pre-processing +- Quiz: Text Pre-processing +- TF-IDF Features +- Quiz: TF-IDF Features +- Word Embeddings +- Create word2vec Features +- Quiz: Word Embeddings +Naïve Bayes +- Introduction to Naive Bayes +- Quiz: Introduction to Naive Bayes +- Conditional Probability and Bayes Theorem +- Working of Naive Bayes +- Quiz: Conditional Probability and Naive Bayes +- Math Behind Naive Bayes +- Types of Naive Bayes +- Implementing Naive Bayes +- Quiz: Types of Naive Bayes +- Project: Naive Bayes +Multiclass and Multilabel +- Understanding how to solve Multiclass and Multilabel Classification Problem +- Quiz: Multiclass and Multilabel +- Evaluation Metrics: Multi Class Classification +- Quiz: Evaluation Metrics for Multi Class Classification +Project: Web Page Classification +- Understanding the Problem Statement +- Understanding the Data +- Building a Web Page Classifier +Basics of Ensemble Techniques +- Introduction to Ensemble +- Quiz: Introduction to Ensemble +- Basic Ensemble Techniques +- Quiz: Basic Ensemble Techniques +- Implementing Basic Ensemble Techniques +- Why Ensemble Models Work Well? +- Quiz: Why do ensemble models work well? +Advance Ensemble Techniques +- Introduction to Stacking +- Implementing Stacking +- Variants of Stacking +- Implementing Variants of Stacking +- Quiz: Variants of Stacking +- Introduction to Blending +- Implementation: Blending +- Quiz: Introduction to Blending +- Bootstrap Sampling +- Quiz: Bootstrap Sampling +- Introduction to Random Forest +- Quiz: Introduction to Random Forest +- Hyper-parameters of Random Forest +- Quiz: Hyper-parameters of Random Forest +- Implementing Random Forest +- Quiz: Implementing Random forest +- Introduction to boosting +- Quiz: Introduction to Boosting +- Gradient Boosting Algorithm (GBM) +- Quiz: Gradient Boosting Algorithm +- Math Behind GBM +- Implementing GBM +- Quiz: Implementing GBM +- Extreme Gradient Boosting (XGBM) +- Implementing XGBM +- Quiz: Implementing XGBM +- Quiz: Extreme Gradient Boosting +- Adaptive Boosting +- Implementing Adaptive Boosting +- Quiz: Adaptive Boosting +Project: Ensemble Model on NYC Taxi Trip Duration Prediction +- Predicting the NYC Taxi Trip Duration +- Prediction the NYC Taxi Trip Duration: Dataset +Share your Learnings +- Write for Analytics Vidhya's Medium Publication +Hyperparameter Tuning +- Introduction to Hyperparameter Tuning +- Different Hyperparameter Tuning methods +- Quiz: Hyperparameter Tuning +- Implementing different Hyperparameter Tuning methods +- Quiz: Implementing different Hyperparameter tuning +Support Vector Machine +- Understanding SVM Algorithm +- Quiz: Support Vector Machine +- SVM Kernel Tricks +- Kernels and Hyperparameters in SVM +- Quiz: Kernels and Hyperparameters in SVM +- Implementing Support Vector Machine +- Quiz: Kernel Tricks +- Project: SVM +Working with Image Data +- Introduction to Images +- Understanding the Image data +- Quiz: Understanding the Image Data +- Understanding transformations on Images +- Understanding Edge Features +- Quiz: Understanding Edge Features +- Histogram of Oriented Features (HOG) +- Quiz: HOG +- Quiz: Image Features +Project: Malaria Detection using Blood Cell Images +- Understanding the Problem Statement +- Detecting Malaria using Blood Cell Images +- Dataset: Malaria Detection using Blood Cell Images +Advance Dimensionality Reduction +- Introduction to Principal Component Analysis +- Steps to perform Principal Component Analysis +- Quiz: Principal Component Analysis +- Computation of the Covariance Matrix +- Quiz: Covariance Matrix +- Finding the Eigenvectors and Eigenvalues +- Quiz: Finding eigenvectors and eigenvalues +- Understanding the MNIST dataset +- Quiz: Introduction to MNIST dataset +- Implementing Principal Component Analysis +- Quiz: Steps to perform PCA +- Introduction to Factor Analysis +- Steps to perform Factor Analysis +- Quiz: Factor Analysis +- Implementing Factor Analysis +- Quiz: Implementing Factor Analysis +Unsupervised Machine Learning Methods +- Introduction to Clustering +- Quiz: Introduction to Clustering +- Applications of Clustering +- Quiz: Applications of clustering +- Evaluation Metrics for Clustering +- Quiz: Evaluation Metrics for Clustering +- Understanding K-Means +- K-Means from Scratch Implementation +- Quiz: Understanding K-Means +- Challenges with K-Means +- Quiz: Challenges with K means clustering +- How to Choose Right k-Value +- Quiz: How to choose the right value of k +- K-Means Implementation +- Quiz: K-Means Implementation +- Hierarchical Clustering +- Implementation Hierarchical Clustering +- Quiz: Hierarchical Clustering +- How to Define Similarity between Clusters +- Quiz: How to define similarity between two clusters +Working with Large Datasets: Dask +- Introduction to Dask +- Quiz: Introduction to dask +- Understanding Dask Array and Dataframes +- Quiz: Understanding Dask arrays and dataframes +- Implementing Dask Array and Dataframes +- Quiz: Implementing Dask array and Dask dataframe +- Machine Learning using Dask +- Quiz: Machine Learning using Dask +- Implementing Linear Regression model using Dask +- Expert Talk: Abhishek Kumar +Automated Machine Learning +- Introduction to Automated Machine Learning +- Quiz: Introduction to automated machine learning +- Introduction to MLBox +- Implementing MLBox +- Quiz: MLBox +Introduction to Neural Network +- Quiz - Perceptron +- Quiz - Weights in Perceptron +- Quiz - Multi Layer Perceptron +- Understanding Decision Boundary +- Quiz: Understanding the decision boundary +- Quiz - Visualizing the neural network +- Forward and Backward Prop Intuition +- Quiz - Forward and Backward Prop Intuition +- Gradient Descent Algorithm +- Quiz - Gradient Descent Algorithm +Forward and Backward Propagation +- Understanding Forward Propagation Mathematically +- Quiz - Understanding Forward Propagation Mathematically +- Understanding Backward Propagation Mathematically +- Quiz - Understanding Backward Propagation Mathematically +- Backward Propagation: Matrix Form +- Why Numpy? +- Quiz: Why Numpy? +- Neural Network From scratch Using Numpy +- Quiz: Implementation of Neural Network +- Forward Propagation (using Numpy) +- Backward Propagation (using Numpy) +- Training network (using Numpy) +Activation Functions +- Why do we need activation functions? +- Quiz - Why do need activation functions +- Linear Activation Function +- Quiz - Linear Activation Function +- Sigmoid and tanh +- Quiz - Sigmoid and tanh +- ReLU and Leaky ReLU +- Quiz - ReLU and LeakyReLU +- Softmax +- Quiz - Softmax +- Tips to selecting right Activation Function +- Quiz: Tips to selecting right activation function +Optimizers +- Variants of Gradient Descent +- Quiz - Variants of Gradient Descent +- Problems with Gradient Descent +- Quiz - Problems with Gradient Descent +- RMSProp +- Quiz - RMSPro +- Adam +- Quiz: Adam +Loss Function +- Introduction to loss function +- Quiz - Introduction to Loss Function +- Binary and Categorical Cross entropy / log loss +- Quiz - Binary and Categorical cross entropy / log loss +Project: NN on structured Data +- Overview of Deep Learning Frameworks +- Quiz - Overview of deep learning frameworks +- Understanding important Kears modules +- Quiz: Understanding important Keras Modules +- Understanding the problem statement: Loan Prediction +- Quiz: Understanding the problem statement : loan prediction +- Data Preprocessing: Loan Prediction +- Quiz - Data Preprocessing: Loan Prediction +- Steps to solve Loan Prediction Challenge +- Loading loan prediction dataset +- Defining the Model Architecture for loan prediction problem +- Quiz: Defining the model architecture for loan prediction problem +- Training and Evaluating model on Loan Prediction Challenge +- Quiz - Training and Evaluating model on Loan Prediction Challenge +Assignment 1 - Big Mart +- Assignment: Big Mart Sales Prediction +Interpretability of Machine Learning Models +- Introduction to Machine Learning Interpretability +- Quiz: Introduction to ML Interpretability +- Framework and Interpretable Models +- Model Agnostic Methods for Interpretability +- Quiz: Model Agnostic Methods for interpretability +- Implementing Interpretable Model +- Quiz: Implementing Interpretable model +- Implementing Global Surrogate and LIME +- Quiz: Implementing Global Surrogate and LIME +- Project: Model Interpretability +Model Deployment +- Introduction to Model Deployment +- Outline of the Module +- Quiz: Outline of the Module +- Understanding the problem statement +- Steps to build the Loan Eligibility Application +- Frontend of the Loan Eligibility App +- Quiz: Frontend of the Loan Eligibility application +- Deploying rule based model using streamlit +- Exercise: Deploying rule based model using Streamlit +- Deploying machine learning model using streamlit +- Exercise: Deploying machine learning model using Streamlit +- Build a Big Mart Sales Prediction Application +- Model Deployment Handout","About Applied Machine Learning - Beginner to Professional CourseMachine Learning is re-shaping and revolutionising the world and disrupting industries and job functions globally. It is no longer a buzzword - many different industries have already seen automation of business processes and disruptions from Machine Learning. In this age of machine learning, every aspiring data scientist is expected to upskill themselves in machine learning techniques & tools and apply them in real-world business problems.Pre-requisites for the Applied Machine Learning courseThis course requires no prior knowledge about Data Science or any tool.Download Brochure" +Ace Data Science Interviews,"Overview - Ace Data Science Interviews +Overview - The 7 step Data Science Interviews process +- Infographic - The 7 Step Framework for Data Science Interviews +Step 1 - Understanding Roles, skills, Interviews Framework +- Overview of Module 3 +- Overview of Different Roles +- Senior Roles in Data Science +- Mid-Management Roles in Data Science +- Individual Contributors in Data Science +- Overview of Different Types of Interviews +- Technical Interviews +- Assignments +- HR Assessment +- Business Case Studies +- Guesstimates +- Puzzles +- Different Interviews for Different Job Roles +- Exercise : Identify Roles +Step 2 - Building Your Digital Presence +- 4.1 Building your Digital Presence +- Ace Data Science Interviews - GitHub Checklist +- Ace Data Science Interviews - LinkedIn Checklist +Step 3 - Building Resume and Applying for Jobs +- 1. Importance of Resume +- 2. 6 Step Process for Crafting your Resume +- 3. Examples of Stand out Resumes +- 4. Live Resume Screening - Example 1 +- 5. Live Resume Screening - Example 2 +- 6. Live Resume Screening - Example 3 +- 7. Overview of the Various Paths to Apply +- 8. Applying to Online Portals +- 9. Networking Based Applications +- 10. Work Based Applications +Step 4 - Telephonic Interviews +- 1. Why Companies Ask for Telephonic Interviews +- 2. Telephonic Interview Checklist - BEFORE the Interview +- 3. Telephonic Interview Checklist - DURING the Interview +- 4. Telephonic Interview Checklist - POST the Interview +- 5. Additional Tips for Video Interviews +- Common Questions Interviewers Ask +- Questions you can Ask the Interviewer +Step 5 - Assignments +- 1. Why Companies Hand Out Assignments +- 2. Assignments for Different Roles +- 3. Tips to Ace the Interview Round +Step 6 - In Person Interview(s) +- 1. Overview of the Different Data Science Interview Types +- 2. Technical Interviews +- 3. Puzzle-Based Interview Rounds +- 4. Tips to Solve Puzzles +- 5. Cracking In-Person Case Studies +- 6. Live In-Person Case Study - Example 1 +- 7. Feedback on the Case Study (Example 1) +- 8. Live In-Person Case Study - Example 2 +- 9. Feedback of the Case Study (Example 2) +- 10. Guesstimates +- 11. HR Round +- The Ultimate Handbook of Data Science Interview Questions +- Download: The Ultimate Handbook of Data Science Interviews Questions +Step 7 - Post Interview Follow ups +- 1. Post-Interview Steps +- 2. Understanding the Different Post-Interview Steps +- Assignment: Share your learning and build your profile","About Ace Data Science Interview CourseAre you trying to get into data science roles but getting rejected by employers? Are you scared of getting into data science interviews? Or don't know what to expect in data science interviews? This is just the course you need.While you might know the tools and techniques in data science, clearing a data science interview might still prove very difficult. You need to show your problem solving skills and technical prowess in these data science interviews.This course has been created based on hundreds of interviews we have taken, companies we have helped in data science interviews and several data science experts in the industry.Key learnings and takeaways from ""Ace Data Science Interviews"" course:Understand different roles existing in data science ecosystem(e.g.Data Scientist, Data Engineers, Data Analyst etc.)Learn what skill sets required for each of these rolesUnderstand different types of Interviews which happen in Data Science IndustryTips and tricks to Ace your Data Science InterviewsHow to build your digital presence including LinkedIn and GitHub profileLearn the process to create a professional experience for data science roles.Framework to solve Guesstimates and case studies used in data science interviewsDownloadable Resources:Infographic for 7 step process to ""Ace Data Science Interviews""e-book containing more than 240 interview questions from interviews in industry.Interview Questions on machine learning, statistics, Model building, Machine Learning production, SQLChecklist for your LinkedIn and GitHub profiles" +Writing Powerful Data Science Articles,"Welcome to the Data Science Writing Crash Course! +- Why did we create this course? +- AI&ML Blackbelt Plus Program (Sponsored) +Crash Course - How to Write Powerful Data Science Articles +- Session #1 - How to Write Powerful and Impactful Data Science Articles +- Session #2 - Writing Data Science Articles that Grab your Reader's Attention +Expert Talk: Writing Powerful Data Science Articles with Parul Pandey +- Expert Talk: Parul Pandey on Writing Powerful Data Science Articles +Next Steps... +- Congrats! Here's what's next","Looking to Publish your Data Science Article? Here’s the Perfect Course for you""Either write something worth reading or do something worth writing."" - Benjamin FranklinThe best way to learn any concept, especially in data science, is by writing about it. That not only helps you understand what you learned in more detail, but sharing it with the community helps others understand how a particular data science idea works.But here’s the thing - most people want to write, but just can’t get past the initial challenges. This might sound familiar to a lot of people:What should I write about?Will anyone read my article?How do I make my article stand out?Should I even write?If you’ve ever asked yourself these questions, you’ll find the answers in this free crash course on how to write impactful and awesome data science articles!" +Machine Learning Certification Course for Beginners,"Overview of the Course +- AI&ML Blackbelt Plus Program (Sponsored) +Introduction to Data Science and Machine Learning +- Overview of Machine Learning / Data Science +- Common Terminology used in Data Science +- Applications of Data Science +Setting up your system +- Installation steps for Windows +- Installation steps for Linux +- Installation steps for Mac +Introduction to Python +- Introduction to Python +- Introduction to Jupyter Notebook +- Download Python Module Handouts +Variables and Data Types +- Introduction to Variables +- Implementing Variables in Python +Operators +- Introduction to Operators +- Implementing Operators in Python +- Quiz: Operators +Conditional Statements +- Introduction to Conditional Statements +- Implementing Conditional Statements in Python +- Quiz: Conditional Statements +Looping Constructs +- Introduction to Looping Constructs +- Implementing Loops in Python +- Quiz: Loops in Python +- Break, Continue and Pass Statements +- Quiz: Break, Continue and Pass Statement +Data Structures +- Introduction to Data Structures +- List and Tuple +- Implementing List in Pyhton +- Quiz: Lists +- List - Project in Python +- Implementing Tuple in Python +- Quiz: Tuple +- Introduction to Sets +- Implementing Sets in Python +- Quiz: Sets +- Introduction to Dictionary +- Implementing Dictionary in Python +- Quiz: Dictionary +String Manipulation +- Introduction to String Manipulation +- Quiz: String Manipulation +Functions +- Introduction to Functions +- Implementing Functions in Python +- Quiz: Functions in Python +- Lambda Expression +- Quiz: Lambda Expressions +- Recursion +- Implementing Recursion in Python +- Quiz: Recursion +Modules, Packages and Standard Libraries +- Introduction to Modules +- Modules: Intuition +- Introduction to Packages +- Standard Libraries in Python +- User Defined Libraries in Python +- Quiz: Modules, Packages and Standard Libraries +Handling Text Files in Python +- Handling Text Files in Python +- Quiz: Handling Text Files +Introduction to Python Libraries for Data Science +- Important Libraries for Data Science +- Quiz: Important Libraries for Data Science +Python Libraries for Data Science +- Basics of Numpy in Python +- Basics of Scipy in Python +- Quiz: Numpy and Scipy +- Basics of Pandas in Python +- Quiz: Pandas +- Basics of Matplotlib in Python +- Basics of Scikit-Learn in Python +- Basics of Statsmodels in Python +Reading Data Files in Python +- Reading Data in Python +- Reading CSV files in Python +- Reading Big CSV Files in Python +- Quiz: Reading CSV files in Python +- Reading Excel & Spreadsheet files in Python +- Quiz: Reading Excel & Spreadsheet files in Python +- Reading JSON files in Python +- Quiz: Reading JSON files in Python +Preprocessing, Subsetting and Modifying Pandas Dataframes +- Subsetting and Modifying Data in Python +- Overview of Subsetting in Pandas I +- Overview of Subsetting in Pandas II +- Subsetting based on Position +- Subsetting based on Label +- Subsetting based on Value +- Quiz: Subsetting Dataframes +- Modifying data in Pandas +- Quiz: Modifying Dataframes +Sorting and Aggregating Data in Pandas +- Preprocessing, Sorting and Aggregating Data +- Sorting the Dataframe +- Quiz: Sorting Dataframes +- Concatenating Dataframes in Pandas +- Concept of SQL-Like Joins in Pandas +- Implementing SQL-Like Joins in Pandas +- Quiz: Joins in Pandas +- Aggregating and Summarizing Dataframes +- Preprocessing Timeseries Data +- Quiz: Preprocessing Timeseries Data +Visualizing Patterns and Trends in Data +- Visualizing Trends & Pattern in Data +- Basics of Matplotlib +- Data Visualization with Matplotlib +- Quiz: Matplotlib +- Basics of Seaborn +- Data Visualization with Seaborn +- Quiz: Seaborn +Machine Learning Lifecycle +- 6 Steps of Machine Learning Lifecycle +- Introduction to Predictive Modeling +Problem statement and Hypothesis Generation +- Defining the Problem statement +- Introduction to Hypothesis Generation +- Performing Hypothesis generation +- Quiz - Performing Hypothesis generation +- List of hypothesis +- Data Collection/Extraction +- Quiz - Data Collection/Extraction +Importance of Stats and EDA +- Introduction to Exploratory Data Analysis & Data Insights +- Quiz - Introduction to Exploratory Data Analysis & Data Insights +- Role of Statistics in EDA +- Descriptive Statistics +- Inferential Statistics +- Quiz - Descriptive and Inferential Statistics +Build Your First Predictive Model +- Quiz: Build a Benchmark Model - Regression +- Benchmark Model: Regression Implementation +- Quiz: Benchmark Model - Regression Implementation +- Build a Benchmark Model: Classification +- Quiz: Build a Benchmark Model - Classification +- Benchmark Model: Classification Implementation +- Quiz: Benchmark - Classification Implementation +Evaluation Metrics +- Introduction to Evaluation Metrics +- Quiz: Introduction to Evaluation Metrics +- Confusion Matrix +- Quiz: Confusion Matrix +- Accuracy +- Quiz: Accuracy +- Alternatives of Accuracy +- Quiz: Alternatives of Accuracy +- Precision and Recall +- Quiz: Precision and Recall +- Thresholding +- Quiz: Thresholding +- AUC-ROC +- Quiz: AUC-ROC +- Log loss +- Quiz: Log loss +- Evaluation Metrics for Regression +- Quiz: Evaluation Metrics for Regression +- R2 and Adjusted R2 +- Quiz: R2 and Adjusted R2 +Preprocessing Data +- Dealing with Missing Values in the Data +- Quiz: Dealing with missing values in the data +- Replacing Missing Values +- Quiz: Replacing Missing values +- Imputing Missing Values in data +- Quiz: Imputing Missing values in data +- Working with Categorical Variables +- Quiz: Working with categorical data +- Working with Outliers +- Quiz: Working with outliers +- Preprocessing Data for Model Building +Build Your First ML Model: k-NN +- Building a kNN model +- Quiz: Building a kNN model +- Determining right value of k +- Quiz: Determining right value of k +- How to calculate the distance +- Quiz: How to calculate the distance +- Issue with distance based algorithms +- Quiz: Issue with distance based algorithms +- Introduction to sklearn +- Implementing k-Nearest Neighbours algorithm +- Quiz: Implementing k-Nearest Neighbours algorithm +Selecting the Right Model +- Introduction to Overfitting and Underfitting Models +- Quiz: Introduction to Overfitting and Underfitting Models +- Visualizing overfitting and underfitting using knn +- Quiz: Visualizing overfitting and underfitting using knn +- Selecting the Right Model +- What is Validation? +- Quiz: What is Validation +- Understanding Hold-Out Validation +- Quiz: Understanding Hold-Out Validation +- Implementing Hold-Out Validation +- Quiz: Implementing Hold-Out Validation +- Understanding k-fold Cross Validation +- Implementing k-fold Cross Validation +- Quiz: Understanding k-fold Cross Validation +- Quiz: Implementing k-fold Cross Validation +- Bias Variance Tradeoff +- Quiz: Bias Variance Tradeoff +Linear Models +- Introduction to Linear Models +- Quiz: Introduction to linear model +- Understanding Cost function +- Quiz: Understanding Cost function +- Understanding Gradient descent (Intuition) +- Maths behind gradient descent +- Convexity of cost function +- Quiz: Convexity of Cost function +- Quiz: Gradient Descent +- Assumptions of Linear Regression +- Quiz: Assumptions of linear model +- Implementing Linear Regression +- Generalized Linear Models +- Quiz: Generalized Linear Models +- Introduction to Logistic Regression +- Quiz: Introduction to logistic regression +- Quiz: Logistic Regression +- Odds Ratio +- Implementing Logistic Regression +- Multiclass using Logistic Regression +- Quiz: Multi-Class Logistic Regression +- Challenges with Linear Regression +- Quiz: Challenges with Linear regression +- Introduction to Regularisation +- Quiz: Introduction to Regularization +- Implementing Regularisation +- Coefficient estimate for ridge and lasso (Optional) +Project: Customer Churn Prediction +- Predicting whether a customer will churn or not +Decision Tree +- Introduction to Decision Trees +- Quiz: Introduction to Decision Trees +- Purity in Decision Trees +- Quiz: Purity in Decision Trees +- Terminologies Related to Decision Trees +- Quiz: Terminologies Related to Decision Trees +- How to Select the Best Split Point in Decision Trees +- Quiz: How to Select the Best Split Point in Decision Trees +- Chi-Square +- Quiz: Chi-Square +- Information Gain +- Quiz: Information Gain +- Reduction in Variance +- Quiz: Reduction in Variance +- Optimizing Performance of Decision Trees +- Quiz: Optimizing Performance of Decision Trees +- Decision Tree Implementation +Feature Engineering +- Introduction to Feature Engineering +- Quiz: Introduction to feature engineering +- Exercise on Feature Engineering +- Overview of the module +- Feature Transformation +- Quiz: Feature Transformation +- Feature Scaling +- Quiz: Feature Scaling +- Feature Encoding +- Quiz: Feature Encoding +- Combining Sparse classes +- Quiz: Combining Sparse classes +- Feature Generation: Binning +- Quiz: Feature Generation- Binning +- Feature Interaction +- Quiz: Feature Interaction +- Generating Features: Missing Values +- Frequency Encoding +- Quiz: Frequency Encoding +- Feature Engineering: Date Time Features +- Implementing DateTime Features +- Quiz: Implementing DateTime Features +- Automated Feature Engineering : Feature Tools +- Implementing Feature tools +- Quiz: Implementing Feature Tools +Project: NYC Taxi Trip Duration prediction +- Exploring the NYC dataset +- Predicting the NYC taxi trip duration +- Predicting the NYC taxi trip duration","What is Machine Learning?Machine Learning is the science of teaching machines how to learn by themselves. Machine Learning is reshaping and revolutionizing the world and disrupting industries and job functions globally.Machine learning is so extensive that you probably use it numerous times a day without knowing it. From unlocking your mobile phones using your face to giving your attendance using a biometric machine, machine learning is being used in almost every stage.In this age of machine learning, every aspiring data scientist is expected to upskill themselves in machine learning techniques & tools and apply them to real-world business problems." +Data Science Career Conclave - Transition to Data Science!,"Career Conclave +- Introduction to the Career Conclave +- Different Roles in Data Science - Which Role is Right For You? +- What are Hiring Managers Really Looking For? +- How to Build your Digital Profile for Data Science +- Panel Discussion: How can you Transition into Data Science in 12 Months? +- AI&ML Blackbelt Plus Program (Sponsored)","Are you looking for a role in the data science space?You’ve come to the right place!It feels like half the world wants to move into data science these days, with spectacular perks and a plethora of openings on offer in the industry. Organizations are investing heavily in data science talent to stay or move ahead of their competitors. As a data science aspirant, you couldn’t have picked a better time to change your career!But this comes with its own set of challenges. We are often asked by folks about how they should transition into data science. People from all sorts of backgrounds – IT, Sales, Finance, HR, Healthcare, etc. – they all want a piece of the data science pie.In this exclusive course called the “Data Science Career Conclave”, Analytics Vidhya has brought together leading data science experts to share their view on a broad range of data science career topics.What is being covered in this Data Science Career Conclave?As we said, a broad range of topics related to transitioning into a data science career. Here’s a brief list of topics you can look forward to:Different Roles in Data Science - Which Role is Right for You? - by Mathangi SriWhat are Hiring Managers Really Looking For? - by Kiran RHow to Build your Digital Profile for Data Science - by Dipanjan SarkarPanel Discussion: How can you Transition into Data Science in 12 Months?" +Top Data Science Projects for Analysts and Data Scientists,"Welcome to the course! +- About the Data Science Projects Course +- AI&ML Blackbelt Plus Program (Sponsored) +Machine Learning Projects +- Machine Learning Visuals – A Brilliant Way to Communicate +- PandaPy – Your New Favorite Python Library +Deep Learning Projects +- VisualDL +- Real-Time Audio Analysis using PyAudio +- OpenAI’s Jukebox: A Generative Model for Music +- Graph Neural Networks in TensorFlow 2.0 +Computer Vision Projects +- Facebook AI's Detectron +- Caire - Image Resizing +- AlphaPose +- FastPhotoStyle +- Facebook AI’s DEtection TRansformer (DETR) +- Real-Time Image Animation +- Convert Any Image into a 3D Photo +- Transform an Image into a Cartoon Illustration +- One-Shot Multi-Object Tracking +- GAN Compression +- StyleGAN2 – A New State-of-the-Art GAN! +- Real-Time Person Removal using TensorFlow.js +- Computer Vision Basics in Microsoft Excel +Natural Language Processing (NLP) Projects +- Open AI's GPT-3 +- NLP Paper Summaries +- Google’s ELECTRA +- Reformer – The Efficient Transformer in PyTorch +Reinforcement Learning Projects +- DeepReinforcementLearning +- Minigo +Data Engineering Projects +- The Goodreads Machine Learning Pipeline +- Awesome Software Engineering for Machine Learning +Other Data Science Projects +- TextShot +- ShyNet – Privacy-Friendly and Cookie-Free Web Analytics +- Coronavirus Time Series Data +- Google Brain AutoML +- ggbump – Data Visualization in R! +- Google Earth Engine – 300+ Jupyter Notebooks to Analyze Geospatial Data +- AVA – Automated Visual Analytics",A Comprehensive Collection of Open Source Data Science Projects!“How many data science projects have you completed so far?”This is a very common question interviewers ask in data science interviews. We have conducted hundreds of these interviews for both data analyst and data scientist roles and this is quite often the jackpot question. This is especially true if you’re a fresher or a relative newcomer to data science.Just doing courses or attaining certifications isn’t good enough. Almost everyone we know holds certifications in various aspects of data science. It adds no value to your resume if you don’t combine it with practical experience.And that’s where open-source data science projects play such a key role! +Getting Started with Git and GitHub for Data Science Professionals,"Getting Familiar with Git and Github +- What is Git? +- What is Github? +- AI&ML Blackbelt Plus Program (Sponsored) +Understand Git Terminology +- What is a Repository? +- Understand Cloning +- Let's Commit! +- Understanding Push +- Understanding Pull +Get Started with Git +- Install Git in your system +- Let's Initialize Git! +- Configure Git in your system +- Committing files in Git +- View Logs in Git +Going Remote - Get started with Github +- Create Remote Repository +- Add Git Remote to Your Repository +- Push using Git +- Cloning a GitHub Repository +- Branching and Merging +- Pull using Git +What's next? +- Forking and contributing to the world","Learn All About Git and GitHub in this Essential Course for Data ScientistsEver heard of version control? It is one of the most important concepts in a data scientist’s daily role - and yet most newcomers and beginners haven’t even come across it! This is a fallacy you must overcome as soon as possible.You need to understand how to navigate through Git and GitHub if you want to make it as a data science professional. While a lot of folks know about these tools (having used them for cloning open source code from Google Research and other top data science organizations), they never really understand their real purpose.The beauty of version control will be akin to a revelation. The way you can create a remote project and have all your team members work on different features parallelly yet independently but still have a stable running code at the end of the day - priceless! A lot of the problem we face in data science while working remotely and independently will be erased with a quick understanding of Git and GitHub.Yes, this course really is that important!" +Title not found,Curriculum not found,An error occurred while fetching the page: 404 Client Error: Not Found for url: https://courses.analyticsvidhya.com/courses/machine-learning-starter-program +"Data Science Hacks, Tips and Tricks","Introduction to Data Science Hacks, Tips and Tricks Course +- About the Data Science Hacks, Tips and Tricks Course +- AI&ML Blackbelt Plus Program (Sponsored) +Data Science Hack #1 - Resource Downloader +- Resource Downloader +Data Science Hack #2 - Pandas Apply +- Pandas Apply +Data Science Hack #3 - how to extract email addresses from text? +- Extract E-mails from text +Data Science Hack #4 - Pandas Boolean Indexing +- Pandas Boolean Indexing +Data Science Hack #5 - Pandas Pivot Table +- Pandas Pivot Table +Data Science Hack #6 - Splitting a String in Python +- str.split() +Data Science Hack #7 - Transforming distributions to Normal Distributions +- Normal Distribution +Data Science Hack #8 - Remove Emojis from text +- Remove Emojis from text +Data Science Hack #9 - Elbow method for kNN classifier +- Elbow method for classifier +Data Science Hack #10 - Pandas crosstab for quick exploratory analysis +- Pandas crosstab +Data Science Hack #11 - Scaling features using MinMax Scaler +- MinMax Scaler +Data Science Hack #12 - Feature Engineering for Date Time Features +- Feature engineering for time series data +Data Science Hack #13 - Creating dummy test data using sklearn +- Dummy data for Linear Regression +Data Science Hack #14 - Image Augmentation to increase size of Training data +- Image Augmentation +Data Science Hack #15 - Fast Tokenization using Hugging Face +- Tokenize by Hugging Face +Data Science Hack #16 - Stratified sampling using sklearn +- Stratify - Splitting data proportionately +Data Science Hack #17 - Reading html files using Pandas read_html +- Reading HTML file +Data Science Hack #18 - Extract different data types into different dataframes +- Divide Continuous and categorical data +Data Science Hack #19 - Pandas profiling for quick exploratory analysis +- Pandas Profilling +Data Science Hack #20 - Change wide form dataframe to Long form dataframe +- Formatting of DataFrames +Data Science Hack #21 - Magic functions in Jupyter notebooks +- Magic function- %history +Data Science Hack #22 - Set Jupyter theme +- Setting up Dark Jupyter notebook theme +Data Science Hack #23 Change Cell width in Jupyter notebook +- Use Jupyter-themes to change cell width +Data Science Hack #24 - Change Datatype to datetime +- Use parse_dates in read_csv +Data Science Hack #25 - Sharing jupyter notebook +- Use Jupyter nbviewer to share ipynb +Data Science Hack #26 - Visualize Decision Tree +- Decision Tree Plotting +Data Science Hack #27 - Invert Dictionary in Python +- Reversing Dictionary +Data Science Hack #28 Visualize Interactive plot +- Interactive Plot using cufflinks +Data Science Hack #29 - Write python file directly from jupyter notebook cell +- Using %%writefile and %run magic functions +Data Science Hack #31 Feature Selection +- Feature Selection using Sklearn's SelectFromModel +Data Science Hack #32 Convert string into characters +- Easiest way to convert string into characters +Data Science Hack #33 Apply pandas in parallel +- Pandarellel - Pandas in parallel +Data Science Hack #34 Convert Date format +- Date Parser +Data Science Hack #35 Make images of same size +- Resize Images +Data Science Hack #36 Regex testing and debugging +- Regex 101","Do you want to write more efficient Python code? Want to become a better programmer? How about speeding up your data science tasks? This Data Science Hacks, Tips and Tricks course is for you!The Data Science Hacks, Tips and Tricks course is your one stop destination to become a better and more efficient data scientist!We have poured in our decades of experience with data science and programming (especially Python programming!), to provide you with time-saving hacks related to:Python tips and tricksData exploration tips and tricksData preprocessing hacksEfficient use of Jupyter notebooksPython functionsBuilding predictive models (hacks to build machine learning models in no time!),And much more!We have created the Data Science hacks, tips and tricks course in a way that you can go through each hack as a separate module. Since the goal of the hacks, tips and tricks is to provide you with efficient code to solve problems, the videos are a demo of these hacks, tips and tricks. The videos are self-explanatory.This free course by Analytics Vidhya covers a broad range of data science hacks, tips and tricks, including Python programming hacks, tips and tricks to ace data science tasks like data preprocessing and data exploration, and much more. Get started today!" +Introduction to Business Analytics,"What is Business Analytics? +- What is Business Analytics? +- Quiz: What is Business Analytics +- You just joined an exicting startup! +- Quiz - Map the Job families +- Data Scientist vs. Data Engineer vs. Business Analyst +- Quiz - Map the responsibilities +- Sample problems and projects - Business Analytics vs. Data Science +- Quiz: Sample problems and Projects - Business Analysts vs. Data Scientits +- A few more things - Business Analytics vs. Data Science +- Career in Business Analytics +- Knowing Each other +- AI&ML Blackbelt Plus Program (Sponsored) +Spectrum of Business Analytics +- Terms related to Business Analytics +- Management Information Systems (MIS) +- Detective Analysis +- Business Intelligence +- Predictive Modeling +- Artificial Intelligence and Machine Learning +- What kind of problems do Business Analysts work on? +Skills Required for Business Analytics and Roadmap of Business Analytics Program +- Skills Required in Business Analytics Roles +- Download the Roadmap for Certified Business Analytics Program (CBAP) +- Bonus Section - Logistics of Certified Business Analytics Program from Analytics Vidhya +Case study: Ezine Publishing +- Overview - Case study - Ezine Publishing +- Understanding Business +- Quiz: Identify Focus Categories +- Quiz: Getting Granular with traffic data +- Quiz - Identify effective channel +- Quiz - Maximize Revenue +- Quiz - Target Customers +- Quiz: Traffic Distribution +- Quiz - Advertisements +- Where to go from here?","About Introduction to Business AnalyticsGetting Started with Business AnalyticsWhat is Business Analytics? Why has it become so popular recently? What are some of the popular applications of Business Analytics? And more importantly, how can you get started with learning Business Analytics from scratch?With growth in digitisation, Business Analytics is ubiquitous right now. Organizations are splurging to integrate data science solutions in their daily processes. This is where they need Business Analysts.Why pursue Business Analytics:Data is ubiquitous! Organizations need people who can use Business Analytics tools and techniques to make sense of this data.It is one of the hottest field in the industry right nowThere are so many Business Analytics tools and techniques which can be applied to solve business problems. Keep learning, keep growing!The potential of Business Analytics is limitless - spanning across industries, roles and functions" +Introduction to PyTorch for Deep Learning,"What is PyTorch? +- Getting Started with PyTorch +- Why should we use PyTorch? +- A word from the creators of PyTorch +- Tensors in PyTorch +- Mathematical Operations in PyTorch(vs. NumPy) +- Matrix Operations in PyTorch(vs. NumPy) +- Tensor Operations +- AI&ML Blackbelt Plus Program (Sponsored) +Neural Networks +- Getting started with Neural Networks +- Exercise : Getting started with Neural Networks +- Independent and Dependent Variables +- Understanding Forward Propagation +- Exercise : Forward Propagation +- Error and Reason for Error +- Exercise : Error and Reason for Error +- Gradient Descent Intuition +- Understanding Math Behind Gradient Descent +- Exercise : Gradient Descent +- Back Propagation +- Exercise : Back Propagation +- Summary of the Module +Implementing a Neural Network in Pytorch +- Modules in PyTorch - Autograd +- Modules in PyTorch: Optim +- Modules in PyTorch: nn +- Implementing a Neural Network from Scratch +Deep Learning on Pytorch +- Case Study – Solving an Image Recognition problem in PyTorch +- Other Use cases for Deep Learning in PyTorch +- What Next?","PyTorch for Deep Learning - A Game Changing Deep Learning FrameworkWelcome to the world of PyTorch - a deep learning framework that has changed and re-imagined the way we build deep learning models.PyTorch was recently voted as the favorite deep learning framework among researchers. It has left TensorFlow behind and continues to be the deep learning framework of choice for many experts and practitioners.PyTorch is super flexible and is quite easy to grasp, even for deep learning beginners. If you work on deep learning and computer vision projects, you’ll love working with PyTorch." +Title not found,Curriculum not found,An error occurred while fetching the page: 404 Client Error: Not Found for url: https://courses.analyticsvidhya.com/courses/introductory-data-science-for-business-managers +Introduction to Natural Language Processing,"Module 1 : Introduction to Natural Language Processing +- Welcome to the Course +- About the Course +- Introduction to Natural Language Processing +- Exercise : Introduction to Natural Language Processing +- Python for Data Science (Optional) +- AI&ML Blackbelt Plus Program (Sponsored) +Module 2: Learn to use Regular Expressions +- Welcome to Module +- Understanding Regular Expression +- Implementing Regular Expression in Python +- Exercise : Implementing Regular Expression in Python +Module 3: First Step of NLP - Text Processing +- Welcome to Module +- Tokenization and Text Normalization +- Exercise : Tokenization and Text Normalization +- Exploring Text Data +- Part of Speech Tagging and Grammar Parsing +- Exercise : Part of Speech Tagging and Grammar Parsing +- Implementing Text Pre-processing Using NLTK +- Exercise : Implementing Text Pre-processing Using NLTK +- Build a Basic ML Model for Text Classification +Module 4: NLP Certification Exam +- NLP Exam +Module 5: Where to go from here? +- Where to go from here?","Introduction to Natural Language Processing (NLP)Natural Language Processing is the art of extracting information from unstructured text. Learn basics of Natural Language Processing, Regular Expressions & text sentiment analysis using machine learning in this course." +Getting started with Decision Trees,"Getting Started with Decision Tree +- Introduction to Decision Tree +- Purity in Decision Trees +- Quiz: Purity in Decision Trees +- Terminologies Related to Decision Trees +- Quiz: Introduction to Decision Trees +- Terminologies Related to Decision Trees +- How to Select the Best Split Point in Decision Trees +- Quiz: How to Select the Best Split Point in Decision Trees +- Chi-Square +- Quiz: Chi-Square +- Information Gain +- Quiz: Information Gain +- Reduction in Variance +- Quiz: Reduction in Variance +- Optimizing Performance of Decision Trees +- Quiz: Optimizing Performance of Decision Trees +- Decision Tree Implementation +- Dataset: Decision Tree Implementation +- Test your Skills: Decision Tree +- Where to go from here? +- AI&ML Blackbelt Plus Program (Sponsored)","What is a Decision Tree?A Decision Tree is a flowchart like structure, where each node represents a decision, each branch represents an outcome of the decision, and each terminal node provides a prediction / label.Why learn about Decision Trees?Decision Trees are the most widely and commonly used machine learning algorithms.Decision Trees can be used for solving both classification as well as regression problems.Decision Trees are robust to Outliers, so if you have Outliers in your data - you can still build Decision Tree models without worrying about impact of Outliers on your model.Decision Trees are easy to interpret and hence have multiple applications in different industries." +Introduction to Python,"Overview of the Course +- Overview of the Course +- AI&ML Blackbelt Plus Program (Sponsored) +Introduction to Python +- A brief introduction to Python +- Introduction to Python Test +- Installing Python +- Become a BlackBelt in Data Science +Understanding Operators +- Theory of Operators +- Exercise +- Understanding Operators in Python +- Operators Test +Variables and Data Types +- Understanding variables and data types +- Variable Test +- Variables and Data Types in Python +- Exercise +Conditional Statements +- Understanding Conditional Statements +- Exercise +- Implementing Conditional Statements in Python +- Conditional Statements test +Looping Constructs +- Understanding Looping Constructs +- Exercise +- Implementing Looping Constructs in Python +- Looping Constructs test +Functions +- Understanding Functions +- Implementing Functions in Python +- Functions test +Data Structure +- A brief introduction to data structure +- Data Structure test +Lists +- Understanding the concept of Lists +- Lists test +- Implementing Lists in Python +- Exercise +Dictionaries +- Understanding the concept of Dictionaries +- Exercise +- Implementing Dictionaries in Python +- Dictionaries test +Understanding Standard Libraries in Python +- Understanding the concept of Standard Libraries +- Libraries test +Reading a CSV File in Python +- Reading a CSV File in Python - Introduction to Pandas +- Reading a CSV file in Python: Implementation +- Reading a csv file in Python test +Data Frames and basic operations with Data Frames +- Understanding dataframes and basic operations +- DataFrames and basic operations test +- Reading dataframes and conduct basic operations in Python +- Reading dataframes and conduct basic operations in Python Test +Indexing a Data Frame +- Indexing a Dataframe +- Indexing DataFrames test +- Exercise +Data Manipulation and Visualization +- Sorting Dataframes +- Merging Dataframes +- Quiz: Sorting and Merging dataframes +- Apply function +- Aggregating data +- Quiz: Apply function and Aggregating data +- Basics of Matplotlib +- Data Visualization using Matplotlib +- Quiz: Matplotlib +- Basics of Seaborn +- Data Visualization using Seaborn +- Quiz: Seaborn +Regular Expressions +- Understanding Regular Expressions +- Quiz: Regular Expressions +- Regular Expressions in Python +- Quiz: Regular Expressions in Python +Cheatsheet for Python +- Cheatsheet for Python +Evaluate +- Instructions +- Quiz +- Python Coding Challenge +- Test your Skills: Python +Feedback +- Poll +Where to go from here? +- Where to go from here?","Learn Python for Data ScienceDo you want to enter the field of Data Science? Are you intimidated by the coding you would need to learn? Are you looking to learn Python to switch to a data science career?You have come to just the right place!Most industry experts recommend starting your Data Science journey with PythonAcross biggest companies and startups, Python is the most used language for Data Science and Machine Learning ProjectsStackoverflow survey for 2019 had Python outrank Java in the list of most loved languagesPython is a very versatile language since it has a wide array of functionalities already available. The sheer range of functionalities might sound too exhaustive and complicated, you don‚Äôt need to be well-versed with them all.Most data scientists have a few go-to libraries for their daily tasks like:for performing data cleaning and analysis - pandasfor basic statistical tools ‚Äì numpy, scipyfor data visualization ‚Äì matplotlib, seaborn" +Loan Prediction Practice Problem (Using Python),"Loan Prediction : Practice Problem +- Introduction to the Course +- Table of Contents +- Problem Statement +- Hypothesis Generation +- Exercise 2 | Discussion +- Getting the system ready and loading the data +- Understanding the Data +- Univariate Analysis +- Bivariate Analysis +- Missing Value and Outlier Treatment +- Evaluation Metrics for Classification Problems +- Model Building : Part I +- Logistic Regression using stratified k-folds cross validation +- Feature Engineering +- Model Building : Part II +- AI&ML Blackbelt Plus Program (Sponsored)","About the courseThis course is designed for people who want to solve binary classification problems. Classification is a skill every Data Scientist should be well versed in.In this course, we are solving a real life case study of Dream Housing Finance. The company deals in all home loans. They have a presence across all urban, semi-urban and rural areas. Customers first apply for a home loan after that company validates the customer's eligibility. The company wants to automate the loan eligibility process (real-time) based on customer detail provided while filling online application form.By the end of the course, you will have a solid understanding of Classification problem and Various approaches to solve the probem" +Big Mart Sales Prediction Using R,"Big Mart Sales +- Overview of the Course +- Table of contents +- Problem Statement +- Hypothesis Generation +- Loading Packages and Data +- Understanding the Data +- Univariate Analysis +- Bivariate Analysis +- Missing Value Treatment +- Feature Engineering +- Encoding Categorical Variables +- PreProcessing Data +- Model Building +- Linear Regression +- Regularized Linear Regression +- Random Forest +- XGBoost +- Summary +- AI&ML Blackbelt Plus Program (Sponsored)","About the courseSales prediction is a very common real life problem that each company faces at least once in its life time. If done correctly, it can have a significant impact on the success and performance of that company.In this course you will be working on the Big Mart Sales Prediction Challenge.The course will equip you with the skills and techniques required to solve regression problems in R. You will be provided with sufficient theory and practice material to hone your predictive modeling skills." +Twitter Sentiment Analysis,"Twitter Sentiment Analysis (Using Python) +- Overview of the Course +- Understand the Problem Statement +- Table of Contents +- Loading Libraries and Data +- Data Inspection +- Data Cleaning +- Story Generation and Visualization from Tweets +- Bag-of-Words Features +- TF-IDF Features +- Word2Vec Features +- Modeling +- Logistic Regression +- Support Vector Machine (SVM) +- RandomForest +- XGBoost +- FineTuning XGBoost + Word2Vec +- Summary +- AI&ML Blackbelt Plus Program (Sponsored)","What is Sentiment Analysis?Sentiment Analysis or Opinion Mining is a technique used to analyse the emotion in a text. We can extract the attitude or the opinion of a piece of text and get insights on it.In the context of machine learning, you can think of Sentiment Analysis as a Classification problem where the text can either have a positive sentiment, a negative sentiment or a neutral one.What are the applications of Sentiment Analysis in the industry?In the age of social media, it is extremely common to comment abouta movie you liked ora book you didn’t like ora product you bought was not up to the mark.Therefore, a lot of companies use sentiment analysis for their products since it provides direct feedback of the customer’s opinion.It is also important to detect and remove hateful content from social media and companies like Twitter, Facebook, etc. extensively use sentiment analysis on a daily basis.On what kind of projects would I implement sentiment analysis?There are a wide variety of projects where you can use Sentiment Analysis. Here are a couple of popular use cases:Sentiment Analysis can not only be used for customer reviews or product feedback, but in other domains as well.Analyzing the sentiments on social media on the US Elections, for example, gives useful insights on which candidates are favoured by the public and which are not.For other interesting problems involving sentiment/emotion detection, you can visit:https://datahack.analyticsvidhya.com/contest/all/What is the range of sentiments that can be observed and analysed?In the earlier days of Natural language processing and Sentiment Analysis, the sentiments could hold only 2 or 3 values: Positive or Negative, and Positive, Neutral or Negative.However, with the advent of deep learning, we can now recognize the subtle emotions in a text as well.This has made tasks like Sarcasm detection, fake news detection etc. popular in research areas of Natural language processingCan I add this project to my resume and use it in my Interview?Sentiment Analysis is one of the most popular applications of Machine Learning and Classification in Natural language processingWe also encourage you to take up more diverse datasets and apply sentiment analysis on them.Sentiment Analysis is also one of the first projects you would learn in your Natural language processing journey and as such is commonly asked in interviews." +Pandas for Data Analysis in Python,"Getting Started with Pandas +- Introduction to the Course +- Pandas Installation +- AI&ML Blackbelt Plus Program (Sponsored) +Dataset Description +- Loan Prediction +- Big Mart Sales +Read & Write Data using Pandas +- Understanding File System & shell commands +- Reading Excel & CSV files +- Writing Data using Pandas +- Quiz: Reading a csv file using Pandas +Pandas Dataframes +- What are Pandas Dataframes & its operations? +- Selecting Columns & Rows in Pandas (Indexing) +- Quiz: DataFrames and basic operations +Data Exploration using Pandas +- Basic Descriptive Statistics using Pandas +- Plotting using Pandas +- Quiz: Data Exploration using Pandas +Data Manipulation using Pandas +- Renaming Column using Pandas +- Sorting Data in Pandas DataFrame +- Binning using Pandas +- Handling Missing Values +- Apply Function in Pandas for Element wise Operations +- Quiz: Pandas Apply Function +Aggregating data using Pandas +- Types of Aggregations in Pandas +- Aggregations using Pandas in action +- Quiz: Aggregations in Pandas +Merging Data using Pandas +- Merging Data in Pandas Dataframes +- Quiz: Merging Data using Pandas +Pandas Cheatsheet +- Pandas Cheatsheet","Learn Pandas - The Most Popular and Useful Python Library for Data SciencePandas is one of the most popular Python libraries in data science. In fact, Pandas is among those elite libraries that draw instant recognition from programmers of all backgrounds, from developers to data scientists.According to a recent survey by StackOverflow, Pandas is the 4th most used library/framework in the world. That is quite an achievement!Pandas is the first library we import when we fire up our Jupyter notebooks (‚Äòimport pandas as pd‚Äô is indelibly etched in our minds!). It is a super flexible tool that enables us to perform data analysis and data manipulation on Pandas dataframes in double-quick time." +Support Vector Machine (SVM) in Python and R,"Introduction to Support Vector Machines +- What are Support Vector Machines? +- Why do we use SVM and how is it better? +- AI&ML Blackbelt Plus Program (Sponsored) +How does SVM work? +- Non-linear Separation and Margins +- Hyperplanes in SVM +- Quiz: Support Vector Machine +SVM Kernels and Hyperparameters +- Types of Kernels used in SVM +- Quiz: Kernel Tricks +Implementing SVM in Python +- Hyperparameter tuning in SVM +- Implementing Support Vector Machine +Implementing SVM in R +- How to implement Support Vector Machine Classifier in R? +Challenges of SVM +- Drawbacks of SVM +- What next?","Learn Support Vector Machines (SVM) in Python and RWant to learn the popular machine learning algorithm - Support Vector Machines (SVM)? Support Vector Machines can be used to build both Regression and Classification Machine Learning models.This free course will not only teach you basics of Support Vector Machines (SVM) and how it works, it will also tell you how to implement it in Python and R.This course on SVM would help you understand hyperplanes and Kernel tricks to leave you with one of the most popular machine learning algorithms at your disposal." +Evaluation Metrics for Machine Learning Models,"Introduction +- Types of Machine Learning +- Why do we need Evaluation Metrics? +- AI&ML Blackbelt Plus Program (Sponsored) +Evaluation Metrics: Classification +- Confusion Matrix +- Quiz: Confusion Matrix +- Accuracy +- Quiz: Accuracy +- Alternatives of Accuracy +- Quiz: Alternatives of Accuracy +- Precision and Recall +- Quiz: Precision and Recall +- F-Score +- Thresholding +- AUC-ROC +- Quiz: AUC-ROC +- Log Loss +- Quiz: Log Loss +- Gini Coefficient +Evaluation Metrics: Regression +- MAE and MSE +- RMSE and RMSLE +- Quiz: RMSE and RMSLE +- R2 and Adjusted R2 +- R2 and Adjusted R2 +What Next? +- Cross-Validation +- The Way Forward","Evaluation Metrics are a Key Part of Machine Learning ModelsEvaluation metrics form the backbone of improving your machine learning model. Without these evaluation metrics, we would be lost in a sea of machine learning model scores - unable to understand which model is performing well.¬†Wondering where evaluation metrics fit in? Here‚Äôs how the typical machine learning model building process works:We build a machine learning model (both regression and classification included)Get feedback from the evaluation metric(s)Make improvements to the modelUse the evaluation metric to gauge the model‚Äôs performance, andContinue until you achieve a desirable accuracy¬†Evaluation metrics, essentially, explain the performance of a machine learning model. An important aspect of evaluation metrics is their capability to discriminate among model results.¬†If you‚Äôve ever wondered how concepts like AUC-ROC, F1 Score, Gini Index, Root Mean Square Error (RMSE), and Confusion Matrix work, well - you‚Äôve come to the right course!" +Fundamentals of Regression Analysis,"Welcome to the course! +- Welcome! +Introduction to Regression +- What is Regression Analysis? +- Why do we use Regression? +- AI&ML Blackbelt Plus Program (Sponsored) +Types of Regression +- How many types of regression techniques do we have? +Linear Regression +- Introduction to Linear Models +- Understanding Cost function +- Understanding Gradient descent (Intuition) +- Maths behind gradient descent +- Convexity of cost function +- Assumptions of Linear Regression +- Implementing Linear Regression +- Generalized Linear Models +Logistic Regression +- Introduction to Logistic Regression +- Odds Ratio +- Implementing Logistic Regression +- Multiclass using Logistic Regression +- Challenges with Linear Regression +Ridge Regression +- What is Ridge Regression? +- Notebook +Lasso Regression +- What is Lasso Regression? +- Implementation +Selecting the Right Model +- How to select the right regression model? +What next? +- What Next?","Learn all about Regression Analysis and the Different Types of RegressionLinear regression and logistic regression are typically the first algorithms we learn in data science. These are two key concepts not just in machine learning, but in statistics as well.¬†Due to their popularity, a lot of data science aspirants even end up thinking that they are the only forms of regression! Or at least linear regression and logistic regression are the most important among all forms of regression analysis.¬†The truth, as always, lies somewhere in between. There are multiple types of regression apart from linear regression:Ridge regressionLasso regressionPolynomial regressionStepwise regression, among others.¬†Linear regression is just one part of the regression analysis umbrella. Each regression form has its own importance and a specific condition where they are best suited to apply.¬†Regression analysis marks the first step in predictive modeling. The different types of regression techniques are widely popular because they‚Äôre easy to understand and implement using a programming language of your choice." +Getting Started with scikit-learn (sklearn) for Machine Learning,"Welcome to the course! +- Welcome to this course +scikit-learn in Python +- What is scikit-learn? +- Components of scikit-learn +- Community / Organizations using scikit-learn +Use of Scikit-learn in Data Science Life Cycle +- Introduction to Data Science Life Cycle +- Scikit-learn for Data Preprocessing +- Treating missing values +- Treating Outliers +- Feature Engineering +- Dimensionality Reduction +Use of Scikit-Learn in Model Building +- Introduction to Model Building and Evaluation +- Regression +- Classification +- Clustering +Machine Learning pipeline using scikit-learn! +- Introduction +- Understanding Problem Statement +- Building a prototype model +- Data Exploration and Preprocessing +- Encode the categorical variables +- Scale the data +- Model Building +- Feature Importance +- Identifying features to build the ML pipeline +- Pipeline Design +- Building Pipeline +- Predict the Target +Next Steps... +- Conclusion","Learn All About sklearn - The Powerful Python Library for Machine LearningScikit-learn, or sklearn for short, is the first Python library we turn to when building machine learning models. Sklearn is unanimously the favorite Python library among data scientists. As a newcomer to machine learning, you should be comfortable with sklearn and how to build ML models, including:Linear Regression using sklearnLogistic Regression using sklearn, and so on.There’s no question - scikit-learn provides handy tools with easy-to-read syntax. Among the pantheon of popular Python libraries, scikit-learn (sklearn) ranks in the top echelon along with Pandas and NumPy.We love the clean, uniform code and functions that scikit-learn provides. The excellent documentation is the icing on the cake as it makes a lot of beginners self-sufficient with building machine learning models using sklearn.In short, sklearn is a must-know Python library for machine learning. Whether you want to build linear regression or logistic regression models, decision tree or a random forest, sklearn is your go-to library." +Convolutional Neural Networks (CNN) from Scratch,"Introduction to Neural Networks +- What is a Neural Network? +- Types of Neural Networks +- Prerequisites +- AI&ML Blackbelt Plus Program (Sponsored) +Introduction to CNNs +- What is a Convolutional Neural Network? +- Why should you use a CNN +Architecture of a CNN +- The Convolutional Layer +- The Pooling Layer +- The Ouput Layer +- Taking a step back: The bigger picture of CNNs +Mathematics behind CNNs +- Transforming the data +- Forward Propagation +- Backpropagation +Implementing a CNN +- Using NumPy +- Using Keras +What Next? +- Implementing a CNN in PyTorch +- More projects with CNN","Learn about Convolutional Neural Networks (CNN) from ScratchConvolutional Neural Networks, or CNN as they’re popularly called, are the go-to deep learning architecture for computer vision tasks, such as object detection, image segmentation, facial recognition, among others. CNNs have even been extended to the field of video analysis!If you are picking one deep learning architecture to learn and are not sure where to start, you should go for convolutional neural networks. Deep learning enthusiasts and experts with CNN knowledge are being widely sourced in the industry.It’s your time to use this CNN skillset and shine!" +Dimensionality Reduction for Machine Learning,"Introduction to the Course +- Introduction +- AI&ML Blackbelt Plus Program (Sponsored) +Introduction to Dimensionality Reduction +- What is Dimensionality Reduction? +- Why is Dimensionality Reduction required? +- Common Dimensionality Reduction Techniques +Feature Selection Techniques +- Missing Value Ratio +- Missing Value Ratio Implementation +- Low Variance Filter +- Low Variance Filter Implementation +- High Correlation Filter +- Backward Feature Elimination +- Backward Feature Elimination Implementation +- Forward Feature Selection +- Forward Feature Selection Implementation +- Random Forest +Factor Based Feature Extraction Techniques +- Introduction to the Module +- Factor Analysis +- Principal Component Analysis +- Independent Component Analysis +Projection Based Feature Extraction Techniques +- Understanding Projection +- ISOMAP +- t- Distributed Stochastic Neighbor Embedding (t-SNE) +- UMAP","Learn All About the Power of Dimensionality ReductionHave you worked on a dataset with more than a thousand features? How about 40,000 features? We are generating data at an unprecedented pace right now and working with massive datasets in machine learning projects is becoming mainstream.This is where the power of dimensionality reduction techniques comes to the fore. Dimensionality reduction is actually one of the most crucial aspects in machine learning projects.You can use dimensionality reduction techniques to reduce the number of features in your dataset without having to lose much information and keep (or improve) the model’s performance. It’s a really powerful way to deal with huge datasets, as you’ll see in this course!Every data scientist, aspiring established, should be aware of the different dimensionality reduction techniques, such as Principal Component Analysis (PCA), Factor Analysis, t-SNE, High Correlation Filter, Missing Value Ratio, among others.So in this beginner-friendly course, you will learn the basics of dimensionality reduction and why you should know dimensionality reduction in machine learning. We will also cover 12 dimensionality reduction techniques! This course is as comprehensive an introduction as you can get!" +K-Nearest Neighbors (KNN) Algorithm in Python and R,"Introduction +- Welcome to the Course +K-NEAREST NEIGHBOUR +- What is KNN? +- Applications of KNN +Steps to Build a K-NEAREST NEIGHBOUR Model +- Steps to build a KNN model +- Determining right value of k +- How to Calculate distance? +- Issues with distance based algorithms +Implementation in Python and R +- Implementation of KNN in Pyhton +- Implementation of KNN in R +What's Next? +- More resources for you","Learn all about the K-Nearest Neighbor (KNN) Algorithm in Machine LearningK-Nearest Neighbor (KNN) is one of the most popular machine learning algorithms. As a newcomer or beginner in machine learning, you’ll find KNN to be among the easiest algorithms to pick up.And despite its simplicity, KNN has proven to be incredibly effective at certain tasks in machine learning.The KNN algorithm is simple to understand, easy to explain and perfect to demonstrate to a non-technical audience (that’s why stakeholders love it!). That’s a key reason why it’s widely used in the industry and why you should know how the algorithm works." +Ensemble Learning and Ensemble Learning Techniques,"Introduction +- Intuition behind Ensemble Learning +- What is Ensemble Learning? +- What models will be covered in the course? +- Quiz: Introduction to Ensemble Learning +- AI&ML Blackbelt Plus Program (Sponsored) +Basic Ensemble Learning Techniques +- Max Voting +- Averaging +- Weighted Average +- Quiz: Basic Ensemble Techniques +Advanced Ensemble Learning Techniques +- Stacking +- Implementing Stacking +- Variants of Stacking +- Blending +- Bootstrap Sampling +- Quiz: Bootstrap Sampling +Advanced Ensemble Learning: Bagging +- What is Bagging? +- Bagging Meta-Estimator +- Random Forest +- Quiz: Random Forest +- Hyper-parameters of Random Forest +- Quiz: Hyper-parameters of Random Forest +- Implementing Random Forest +Advanced Ensemble Learning: Boosting +- Introduction to boosting +- What is Boosting? +- Quiz: Introduction to Boosting +- Gradient Boosting Algorithm (GBM) +- Math Behind GBM +- Quiz: Gradient Boosting Algorithm +- Extreme Gradient Boosting (XGBoost) +- Implementing XGBoost +- Quiz: XGBoost +- AdaBoost: Adaptive Boosting +- Implementing AdaBoost +- Quiz: AdaBoost +- LightGBM +- CatBoost +What next? +- Next Steps","A Comprehensive Course on Ensemble LearningEnsemble learning is a powerful machine learning algorithm that is used across industries by data science experts. The beauty of ensemble learning techniques is that they combine the predictions of multiple machine learning models.You must have used or come across several of these ensemble learning techniques in your machine learning journey:-Bagging- Boosting- Stacking- Blending, etc.These ensemble learning techniques include popular machine learning algorithms such as XGBoost, Gradient Boosting, among others. You must be getting a good idea of how vast and useful ensemble learning can be!" +Linear Programming for Data Science Professionals,"Introduction to Linear Programming +- How to Use the Mini-Course Template +- AI&ML Blackbelt Plus Program (Sponsored) +Introduction +- Introduction to Linear Programming +- What is Linear Programming +- Formulating a problem – Let’s manufacture some chocolates +- Common Terminologies in Linear Programming +- Process to Formulate a Linear Programming Problem +Tools to Solving Linear Programming Problems +- Using Graph: Problem +- Using Graph : Solution +- Using R Programing: Problem +- Using R Programing: Solution +- Using Open-Solver(Excel):Problem +- Using Open-Solver(Excel): Solution +Methods to Solve Linear Programing Problems +- Method 1 - Simplex Method(Question) +- Simplex method: Solution +- Method 2 - Northwest Corner Method(Problem) +- Northwest Corner Method: Solution +- Method 3 - Least Cost Method +Applications of Linear Programming +- Applications +Conclusion +- What Next?","Get a Head Start on Linear Programming to Solve Optimization Problems in Data Science!Optimization is the way of life. We all have finite resources and time and we want to make the most of them. From using your time productively to solving supply chain problems for your company – everything uses optimization.And that’s where learning linear programming will make you a better data science professional.We are solving optimization problems everyday - without realizing it. Think of how you distributed the chocolate among your peers or siblings - that’s your way of optimizing the situation. On the other hand devising inventory and warehousing strategy for an e-tailer can be very complex. Millions of SKUs with different popularity in different regions to be delivered in defined time and resources.And linear programming helps us solve these optimization problems with ease and efficiency. As a data science professional, you are bound to come across these optimization problems that you will solve using linear programming.Simply put, you should know what linear programming is, and the different methods to solve linear programming problems." +Naive Bayes from Scratch,"Probability +- Key Terms and Definitions +- Introduction to Probability +- Quiz: Introduction to probability +- Calculating Probabilities of events +- Quiz: Calculating Probabilities of events +- AI&ML Blackbelt Plus Program (Sponsored) +The Naive Bayes Algorithm +- Introduction to Naive Bayes +- Quiz: Introduction to Naive Bayes +- Conditional Probability and Bayes Theorem +- Working of Naive Bayes +- Quiz: Conditional Probability and Naive Bayes +- Math Behind Naive Bayes +- Types of Naive Bayes +- Quiz: Types of Naive Bayes +- Implementing Naive Bayes +- Pros and Cons of Naive Bayes +- Applications of Naive Bayes +- Improve your Naive Bayes Model +What Next? +- More Resources and Next Steps","Learning Naive Bayes from Scratch for Machine LearningNaive Bayes ranks in the top echelons of the machine learning algorithms pantheon. It is a popular and widely used machine learning algorithm and is often the go-to technique when dealing with classification problems.The beauty of Naive Bayes lies in it’s incredible speed. You’ll soon see how fast the Naive Bayes algorithm works as compared to other classification algorithms. It works on the Bayes theorem of probability to predict the class of unknown datasets. You’ll learn all about this inside the course!So whether you’re trying to solve a classic HR analytics problem like predicting who gets promoted, or you’re aiming to predict loan default - the Naive Bayes algorithm will get you on your way." +Learn Swift for Data Science,"Introduction +- Getting Started +- Why Swift? +- AI&ML Blackbelt Plus Program (Sponsored) +Swift Basics for Data Analysis +- The Swift Ecosystem +- Setting up the Environment +- Basics of Swift programming - I +- Basics of Swift programming - II +- Python with Swift +Machine Learning with Swift and TensorFlow +- The Swift4Tensorflow Library +- About the Dataset and Setup +- Implementation of MNIST Image Classification +Bonus Chapter: NLP Based iOS Apps uwing Swift +- Introduction +- Setting up the system +- Basic Text Processing +- Language Identification in iOS +- Spell Checking and Correction +- Part Of Speech (POS) Tagging +- Identifying People, Organization, etc. from the Text (Named Entity Recognition) +- Performing Sentiment Analysis on iOS +- Word Embeddings +What Next? +- Next Steps","Your Guide to Learning Swift for Data Science from Scratchhe Swift programming language is quickly becoming the language of choice for a lot of data science experts and professionals. Swift’s flexibility, ease of use, excellent documentation, and quick execution speed are key reasons behind the language’s recent prominence in the data science space.Swift is a more efficient, stable and secure programming language as compared to Python. In fact, Swift is also a good language to build for mobile. In fact, it’s the official language for developing iOS applications for the iPhone!The cherry on the cake for Swift? It has the support of the likes of Google, Apple, and FastAI behind it!“I always hope that when I start looking at a new language, there will be some mind-opening new ideas to find, and Swift definitely doesn’t disappoint. Swift tries to be expressive, flexible, concise, safe, easy to use, and fast. Most languages compromise significantly in at least one of these areas.” – Jeremy HowardAnd when Jeremy Howard endorses a language and starts using it for his daily data science work, you need to drop everything and listen.In this free course on Swift for Data Science, we will learn about Swift as a programming language and how it fits into the data science space. If you’re a Python user, you’ll notice the subtle differences and the incredible similarities between the two. We showcase Swift code as well in the course so get started!" +Introduction to Web Scraping using Python,"Introduction to Web Scraping +- What is Web Scraping? +- Caution +- Popular Libraries for Web Scraping +- Components of Web Scraping +- AI&ML Blackbelt Plus Program (Sponsored) +Web Scraping: Procedure +- Problem Setup +- Step 1: Crawl +- Step 2: Parse and Transform +- Step 3: Store the Data +Scraping URLs and Email IDs from a Web Page +- Single Webpage Scraping +- Multiple Webpage Scraping(BeautifulSoup and Regex) +Scrape Images in Python +- Scrape Images in Python +Scrape Data on Page Load +- Scarpe Data on Page Load","Become Familiar with Web Scraping using PythonThe need and importance of extracting data from the web is becoming increasingly loud and clear. There is an unprecedented volume of data on the internet right now - and data science projects often need this data to build predictive models.That’s a key reason why data scientists are expected to be familiar with web scraping.We have found web scraping to be a very helpful technique for gathering data from multiple websites. Some websites these days also provide APIs for many different types of data you might want to use, such as Tweets or LinkedIn posts.But there might be occasions when you need to collect data from a website that does not provide a specific API. This is where having the ability to perform web scraping comes in handy. As a data scientist, you can code a simple Python script and extract the data you’re looking for.So knowing how to perform web scraping using Python will help you go a long way towards becoming a resourceful data scientist. Are you ready to take the next step and dive in?A note of caution here – web scraping is subject to a lot of guidelines and rules. Not every website allows the user to scrape content so there are certain legal restrictions at play. Always ensure you read the website’s terms and conditions on web scraping before you attempt to do it.In this course, we will dive into the basics of web scraping using Python. We will understand what web scraping is, the different Python libraries for performing web scraping, and finally we’ll implement web scraping using Python in a real-world project. There’s a lot to unpack here so enroll today and start learning!" +Tableau for Beginners,"Introduction +- Welcome to the Course +- AI&ML Blackbelt Plus Program (Sponsored) +Concept of Visualization +- What is Data Visualization and Why Should we Use it +- Hans Rosling - 200 Countries 200 Years 4 Minutes +Understanding the Length and Breadth of Tableau +- Navigating the Tableau Interface Part 1 +- Navigating the Tableau Interface Part 2 +Getting Started with Tableau +- Connect to the Data +- Data Visualizations +Different Types of Charts in Tableau +- Net Statistics +- Net Statistics Part 2 +- Line Chart +- Pie Chart +- Map Chart +- Scatter Plots +BONUS: Other Functionalities in Tableau +- Filters +- Trend Line +What's Next? +- What's Next?","Get Started with Tableau for Data Visualization, Analytics and Business IntelligenceTableau is the gold standard in business intelligence, analytics and data visualization tools. Tableau Desktop (and now Tableau Public) have transformed the way we interact with visualizations and tell data stories to our clients, stakeholders, and to non-technical audiences around the world.Tableau has been recognized as a Leader in the Gartner Magic Quadrant for Analytics and Business Intelligence Platforms for 8 straight years. Here‚Äôs Gartner‚Äôs most recent ranking in 2020:In this Tableau for Beginners course, you will learn everything you need to get started with this wonderful visualization and business intelligence tool. You‚Äôll be able to build charts like bar charts, line charts (for working with time series data), pie charts, and even get the hang of geospatial analysis using map visualizations in Tableau!¬†Note: If you‚Äôre looking to build and master dashboards and storyboards in Tableau, make sure you check out the popular ‚ÄòMastering Tableau from Scratch: Become a Data Visualization Rockstar‚Äô course!" +Getting Started with Neural Networks,"Introduction to Deep Learning +- What is Deep Learning? +- Difference b/w Deep Learning and Machine Learning +- Why Deep Learning is so popular? +- AI&ML Blackbelt Plus Program (Sponsored) +Getting ready for the course +- Hardware for Computations in Deep Learning +- Setting up your system +- Introduction to Google Colab +- Understanding Google Colab Interface +- Pre-requisites for Deep Learning +Introduction to Neural Network +- Perceptron +- Quiz - Perceptron +- Weights in Perceptron +- Quiz - Weights in Perceptron +- Multi Layer Perceptron +- Quiz - Multi Layer Perceptron +- Forward and Backward Prop Intuition +- Quiz - Forward and Backward Prop Intuition +- Gradient Descent Algorithm +- Quiz - Gradient Descent Algorithm +Activation Functions +- Why do we need activation functions? +- Quiz - Why do need activation functions +- Linear Activation Function +- Quiz - Linear Activation Function +- Sigmoid and tanh +- Quiz - Sigmoid and tanh +- Softmax +- Quiz - Softmax +Loss Function +- Introduction to loss function +- Quiz - Introduction to Loss Function +- Binary and Categorical Cross entropy / log loss +- Quiz - Binary and Categorical cross entropy / log loss +NN on structured Data +- Understanding Problem Statement: Loan Prediction +- Data Preprocessing: Loan Prediction +- Quiz - Data Preprocessing: Loan Prediction +- Steps to solve Loan Prediction Challenge +- Loading loan prediction dataset +- Defining the Model Architecture for loan prediction problem +- Training and Evaluating model on Loan Prediction Challenge +- Quiz - Training and Evaluating model on Loan Prediction Challenge +Assignment: Big Mart Sales Prediction +- Assignment: Big Mart Sales Prediction +Real World Use cases of Deep Learning +- Object Detection, segmentation, image generation +- Quiz - Object Detection, Segmentation, image generation +- Sequential Modeling +- Quiz - Sequential Modeling +- Test Your Neural Network Skills +Where to go from here? +- Where to go from here?","Introduction to Neural NetworksWhat is a neural network? How does it work? What does a neural network do? Learn neural networks for free in this course and get your neural network questions answered, including applications of neural networks in deep learning.Learn how neural networks work in deep learningDo you want to acquire a super power? How about learning neural networks? Neural networks are at the heart of the deep learning revolution that’s happening around us right now.Neural networks are the present and the future. The different neural network architectures like convolutional neural networks (CNN), recurrent neural networks (RNN), and others have altered the deep learning landscape.But as a beginner in this field, you’ll have a ton of questions:What is a neural network?Why do we need to learn neural networks?How popular are neural networks?What are the advantages of neural networks?What kind of challenges you could face when applying neural networks?What exactly should you learn about neural networks?What are the core concepts that make up neural networks?What are the different types of neural networks in deep learning?Do you need to know programming to build a neural network?Which programming language is best for building neural networks? Python or R?What are the different applications of neural networks?What kind of problems or projects can you solve using neural networks?From classifying images and translating languages to building a self-driving car, neural networks are powering the world around us. Thanks to the idea of neural networks like CNN and RNN, deep learning has penetrated into multiple and diverse industries, and it continues to break new ground on an almost weekly basis!" +Introduction to AI & ML,"Introduction to AI & ML +- What is AI&ML? +- Types of ML +- When to Apply AI&ML +- Recent AI Uprising +- How the world is Changing? +- Building Blocks of AI and ML +- Knowing Each Other +- AI&ML Blackbelt Plus Program (Sponsored) +Common Terminologies, Tools and Techniques +- Common Terminologies +- Common Data Capturing Types and Tools +- Common Tools +- Common Techniques +- Common Techniques - Part1 +- Common Techniques - Part2 +Skills required to become a data science professional +- Skills Required in Data Science +- AI and ML Black Belt+ +- Where to Go from here!!","Welcome to the World of Artificial Intelligence and Machine Learning!The AI revolution is here - are you prepared to integrate it into your skillset? How can you leverage it in your current role? What are the different facets of AI and ML?Analytics Vidhya’s ‘Introduction to AI and ML’ course, curated and delivered by experienced instructors with decades of industry experience between them, will help you understand the answers to these pressing questions.Artificial Intelligence and Machine Learning have become the centerpiece of strategic decision making for organizations. They are disrupting the way industries and roles function - from sales and marketing to finance and HR, companies are betting big on AI and ML to give them a competitive edge.And this, of course, directly translates to their hiring. Thousands of vacancies are open as organizations scour the world for AI and ML talent. There hasn’t been a better time to get into this field!" +Winning Data Science Hackathons - Learn from Elite Data Scientists,"Introduction to Winning Data Science Hackathon Course +- About the Winning Data Science Hackathon course +- AI&ML Blackbelt Plus Program (Sponsored) +Talks by Elite Data Scientists +- Effective Feature Engineering – A Structured Approach to Building Better ML Models - By Dipanjan Sarkar +- Automating the Machine Learning Pipeline with AutoML -By Dr. Sunil Kumar Chinnamgari +- Panel Discussion - What Sets the Top Hackers Apart? +- Top Hacks from a Kaggle Grandmaster by Pavel Pleskov +- Feature Engineering for Image Data by Aishwarya Singh & Pulkit Sharma","About the courseThere is no substitute for experience. And that holds true in Data Science competitions as well. These cut-throat hackathons require a lot of trial-and-error, effort, and dedication to reach the ranks of the elite.This course is an amalgamation of various talks by top data scientists and machine learning hackers, experts, practitioners, and leaders who have participated and won dozens of hackathons. They have already gone through the entire learning process and they showcase their work and thought process in these talks.This course features top data science hackers and experts, including Sudalai Rajkumar (SRK), Dipanjan Sarkar, Rohan Rao, Kiran R and many more!From effective feature engineering to choosing the right validation strategy, there is a LOT to learn from this course so get started today!" +Hypothesis Testing for Data Science and Analytics,"Introduction to the course +- Introduction to Hypothesis Testing Course +- AI&ML Blackbelt Plus Program (Sponsored) +Fundamentals of Hypothesis Testing +- Understanding Hypothesis Testing +- Steps to Perform for Hypothesis testing +- Critical Value - p-value +- Directional Hypothesis +- Non-Directional Hypothesis +What is the Z Test? +- What is Z test? +- One-Sample Z test +- One-Sample Z test - Example +- Two-Sample Z Test +- Two-Sample Z Test - Example +What is the t-Test? +- What is t-test? +- One-Sample t-Test +- One Sample t-Test - Example +- Two-Sample t-Test +- Two-Sample t-Test - Example +Deciding between Z Test and T-Test +- Deciding between Z Test and T-Test +Case Study: Hypothesis Testing for Coronavirus using Python +- Two-Sample Z test for a Coronavirus Dataset","Statistics is the study of the collection, analysis, interpretation, presentation, and organisation of data. For all the data science and machine learning enthusiasts it is paramount to be well versed with various statistical concepts such as Hypothesis testingEvery day we find ourselves testing new ideas, finding the fastest route to the office, the quickest way to finish our work, or simply finding a better way to do something we love. The critical question, then, is whether our idea is significantly better than what we tried previously.These ideas that we come up with on such a regular basis – that’s essentially what a hypothesis is. And testing these ideas to figure out which one works and which one is best left behind, is calledhypothesis testing."