Hands-on Machine Learning¶
| Chapter | Name | (Poetic1) Description |
|---|---|---|
| 1 | ML Landscape | Some technical Jargon. (Not making it prolly) |
| 2 | End to End ML Project | How to work out a project from beginning to end. Most kinds of operations you'd expect to do. |
| 3 | Classification | Running classification models. MNIST dataset. |
| 4 | Training Models | Under the hood. Regression and stuff |
| 5 | Decision Trees | Simple yet powerful. CART, Regularization, Estimating class probabilities |
| 6 | Support Vector Machines | Using support vectors to define wide streets. |
| 7 | Ensemble Learning & Random Forests | Combining models and making them even more powerful. |
| 8 | Dimensionality Reduction | Make it simple but not simpler. Train them faster and avoid the unnecessary noise. |
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