The Orange Book of Machine Learning - Green edition
The essentials of making predictions using supervised regression and classification for tabular data.
The essentials of making predictions using supervised regression and classification for tabular data. Tech stack: python, pandas, scikit-learn, CatBoost, LightGBM, XGBoost
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About the Book
This 'Green' edition is actively maintained and will be subject to updates. Purchasers have permanent access to the very latest version and (should) be notified by email when an updated version is available for download (latest version: 2.2.0).
The book covers the following topics in the context of supervised learning for tabular data:
1. Introduction
2. Statistics
3. Exploratory data analysis (EDA)
4. Data cleaning
5. Cross-validation
6. Interpolation and smoothing
7. Regression
8. Classification
9. Ensemble estimators
10. Hyperparameter optimization (HPO)
11. Feature engineering and selection
12. Why no neural networks/deep learning?
Book details:
- Tech stack: python, pandas, scikit-learn, CatBoost, LightGBM, XGBoost
- 186 pages, 12pt font size
- A4 page size (210 x 297 mm / 8.3 x 11.7 in) suitable for printing. The pages are `long' (i.e. narrow top and bottom margins) so as to facilitate uninterrupted scrolling when read on a tablet
- Over 130 original figures
- No generative AI whatsoever has been used in the writing of this book
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