What is the difference between supervised, unsupervised, and reinforcement learning?

🧠 1. Supervised Learning

🔍 What it is:

In supervised learning, the machine is trained using labeled data — that means the input data already comes with the correct answer (also called the “label”).

📊 Examples:

• Email spam detection – Emails are labeled as “spam” or “not spam”.
• House price prediction – You have past data with house size and price, and you want to predict price for new houses.
• Image classification – A dataset of cat and dog images, where each image is labeled as “cat” or “dog”.

📌 Algorithms:

• Linear Regression
• Logistic Regression
• Decision Trees
• Support Vector Machines
• Random Forests
• Neural Networks

🧠 2. Unsupervised Learning

🔍 What it is:

In unsupervised learning, the machine is given data without labels. It tries to find hidden patterns or structure in the data.

📊 Examples:

• Customer segmentation – Group customers into similar behavior types without knowing in advance what those groups are.
• Anomaly detection – Detect fraudulent credit card transactions that look different from the norm.
• Topic modeling – Grouping news articles based on similar topics, even though you don’t know the topics beforehand.

📌 Algorithms:

• K-Means Clustering
• Hierarchical Clustering
• DBSCAN
• PCA (for dimensionality reduction)

🧠 3. Reinforcement Learning

🔍 What it is:

In reinforcement learning, an agent learns by interacting with an environment. It takes actions, gets rewards or penalties, and learns which actions lead to the best rewards over time.

📊 Examples:

• Self-driving cars – Learn to drive by making decisions and getting feedback based on performance.
• Game playing (like chess or video games) – The agent learns strategies to win.
• Robotics – A robot learns to walk or pick up objects by trial and error.

📌 Key Concepts:

• Agent, Environment, Actions, States, Rewards
• Q-Learning
• Deep Q Networks (DQN)

📋 Summary Table: