Machine learning
How models learn from data: loss, gradients, training loops, clustering. Small enough to read in one sitting.
| # | Lesson | You’ll learn | Video |
|---|---|---|---|
| 00 | What is machine learning | What “learning from examples” means, with a line that fits itself to 40 house prices | 35s |
| 01 | Gradient descent | How every model improves: feel the slope, take a small step, repeat | 32s |
| 07 | Linear regression, no libraries | The full training loop in plain Python, and what breaks it | 80s + 32s |
| 02 | K-means clustering | Finding groups in data nobody labeled | 31s |
| 08 | Decision trees | How a tree picks its questions with Gini impurity, built from scratch | 137s |
| 09 | A neural network from scratch | XOR, two layers and backpropagation written out by hand | 145s |
| 10 | Overfitting | Why a perfect training score is a red flag, and how validation catches it | 142s |
| 11 | Choosing a model | Picking an algorithm for production: data first, goal second, model last | 143s |