Ch 4 · How Models Learn — Gradient Descent · Machine Learning & AI

Topic 4 of 13 in Machine Learning & AI — Foundations — 2 lessons.

Walking Downhill

Most models can't be solved with a neat formula, so they learn by nudging. Picture the error as a valley: gradient descent feels which way is downhill (the gradient) and takes a small step that way, again and again, until the error stops dropping. The step size is the learning rate.

Training a Line by Hand

Here is the whole idea in ~10 lines: start with w=0, b=0, and repeatedly nudge them down the error slope.

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