• Contents:

Supervised Learning

• 🔧 Learn a mapping function
  • Can predict output for new unseen input data
• Needs labeled data
  • very powerful
  • difficult to perform on millions of datapoints

Regression Algorithms

• 🔧 Predicts numeric value based on input data
  • Continuous output variable
• 💡Linear regression (straight line to describe relationship) very common, but many other types of regression
• Examples
  • Predicting House Prices
  • Stock Price Prediction
  • Weather Forecasting
• Simple linear regression diagram

Classification Algorithms

• 🔧 Predicts categorical label of input data
  • Discrete output variable
• Types and examples
  • Binary Classification (e.g. classify emails as “spam” or “not spam”)
  • Multi-class Classification (e.g. classify animals in a zoo as "mammal", "bird" or "reptile”)
  • Multi-label Classification (e.g. assign multiple labels to a movie, like "action" and "comedy”)
• Binary classification example diagram

Training, Validation and Test Sets

1. Training Set: trains the model
2. Validation Set: tunes model parameters and validates performance
   • ❗ Validation sets are optional
3. Test Set: evaluates final model performance

• Typical percentages of original dataset: 60-80% Training, 10-20% Validation, 10-20% Test