Ch 2 · Data — Features, Labels & Splitting · Machine Learning & AI

Topic 2 of 13 in Machine Learning & AI — Foundations — 2 lessons.

Features and Labels

Every row of training data has two parts: the inputs a model learns from, called features, and the answer you want it to predict, called the label. Telling them apart is the first step in any supervised project. In the example, each row pairs hours studied with a pass (1) or fail (0), and the code separates them into a features list and a labels list.

Train / Validation / Test

To know if a model really learned (vs memorised), you hide some data from it. Split your examples into three buckets:

All topics in Machine Learning & AI Beginner

1. What Is Machine Learning?
2. Data — Features, Labels & Splitting
3. Linear Regression — Fitting a Line
4. How Models Learn — Gradient Descent
5. Classification — Predicting Categories
6. Decision Trees
7. Evaluating a Model Honestly
8. Overfitting & the Bias-Variance Tradeoff
9. The ML Workflow
10. Mini-Project — A Spam Classifier
11. Clustering — Finding Groups Without Labels
12. Your First Real scikit-learn Model
13. Words as Numbers — A Taste of NLP