Update pages/2.Introduction to Data Science.py

pages/2.Introduction to Data Science.py

@@ -37,5 +37,47 @@ st.write("""
 """)
 
 
+st.subheader(":red[**What do you mean by Machine Learning**]")
+st.write("**Machine learning**- is a subset of AI, which uses algorithms that learn from data to make predictions. These predictions can be generated through supervised learning, where algorithms learn patterns from existing data, or unsupervised learning, where they discover general patterns in data. ML models can predict numerical values based on historical data, categorize events as true or false, and cluster data points based on commonalities.")
+st.subheader(":red[**Examples of Machine Learning**]")
+st.write("""
+- Gmail uses ML to identify and filter spam emails.
+- Siri or Google Assistant learns to understand your voice commands better over time.
+- Amazon shows products you might like based on your past purchases.
+- Banks use ML to detect unusual transactions and prevent fraud.
+- Cars use ML to understand road signs
+""")
+
+st.subheader(":red[**Types of Machine Learning**]")
+st.write("Machine Learning can be divided into three main types based on how the model learns from data:")
+
+st.subheader("**1. Supervised Learning**")
+st.write("In supervised learning, the machine is trained using labeled data, meaning the data comes with input-output pairs. The model learns to predict the output from the input by identifying patterns in the data.")
+st.write("""**Examples:** 
+- Predicting house prices based on size, location, and number of rooms.
+- Email classification: Sorting emails as "spam" or "not spam."
+""")
+
+st.subheader("**Types of Supervised Learning:**")
+st.write("**1.Regression:** Predicting continuous values.Example: Predicting a person’s salary based on their experience.")
+st.write("**2.Classification:** Predicting discrete categories.Example: Determining whether an email is spam or not.")
+
+st.subheader("**2. Unsupervised Learning**")
+st.write("In unsupervised learning, the data has no labels. The model identifies hidden patterns or groups within the data without guidance.")
+st.write("""**Examples:**
+- Customer segmentation: Grouping customers based on their buying behavior.
+- Recommendation systems: Suggesting products based on similar users.
+""")
+
+st.subheader("**Types of Unsupervised Learning:**")
+st.write("**1.Clustering:** Grouping data points into similar clusters.Example: Grouping movies by genre based on viewers’ preferences.")
+st.write("**Dimensionality Reduction:** Reducing the number of features in the data.Example: Compressing image data for faster processing.")
+
+st.subheader("**3. Reinforcement Learning**")
+st.write("Reinforcement learning is about learning by trial and error. The model learns to make a sequence of decisions to maximize a reward.")
+st.write("""**Examples:**
+- Learning to drive by interacting with the road environment.
+- Training a robot to walk by rewarding good steps.
+""")