What is Machine Learning?

Machine Learning (ML) is a subset of Artificial Intelligence that gives computers the ability to learn from data and improve performance on tasks without being explicitly programmed. Instead of writing hand-crafted rules, you show the algorithm examples and it discovers patterns automatically.

Supervised Learning — Model trains on labeled input-output pairs (X → y). Examples: spam detection, price prediction, image classification. Unsupervised Learning — Model finds hidden structure in unlabeled data. Examples: customer segmentation, anomaly detection, PCA. Reinforcement Learning — Agent learns by interacting with an environment, receiving rewards or penalties. Examples: game playing (AlphaGo), robotics, ad bidding.

1. Define the problem and success metric 2. Collect and label data 3. Exploratory Data Analysis (EDA) 4. Feature engineering and preprocessing 5. Choose and train a model 6. Evaluate on held-out test data 7. Hyperparameter tuning 8. Deploy and monitor

The simplest supervised learning model — fits a line to data to predict continuous values.