python-machine-learning-book
The "Python Machine Learning (1st edition)" book code repository and info resource
Jupyter Notebook code examples from the first edition of 'Python Machine Learning' by Sebastian Raschka, covering classification, clustering, neural networks, and text analysis using scikit-learn and NumPy.
This repository holds the code examples from the first edition of "Python Machine Learning" by Sebastian Raschka, published by Packt Publishing in 2015. The book is 454 pages and covers machine learning from theory through working code. The code here is meant to accompany the printed or digital book, not to stand on its own, since the notebooks contain code but not the explanatory text and mathematical formulas from the book itself.
The content spans 13 chapters. Topics include training classification algorithms, preparing and cleaning data, reducing the number of variables in a dataset, evaluating how well a model works, combining multiple models to improve accuracy, analyzing text sentiment, embedding a trained model into a web application, regression for predicting numerical values, grouping unlabeled data, and training neural networks for image recognition. The final chapter covers speeding up neural network training using a library called Theano.
The main Python libraries used throughout the examples are NumPy (for numerical operations), scikit-learn (a widely used machine learning toolkit), and Theano (a numerical computation library that can use a graphics card to speed up calculations). Each chapter has its own folder with Jupyter Notebook files that can be opened interactively in a browser.
This repository covers the first edition only. A second edition exists in a separate GitHub repository with updated content. The book has been translated into German, Japanese, Italian, Chinese (both traditional and mainland editions), Korean, and Russian.
The repository also links to free supplementary math and NumPy materials the author prepared for a separate book, covering algebra basics, calculus, and an introduction to NumPy, for readers who want background on the underlying mathematics.
Where it fits
- Follow along with the 'Python Machine Learning' textbook by running the chapter notebooks interactively.
- Study working code examples for classification, regression, clustering, and neural networks in Python.
- Learn how to evaluate model accuracy and combine multiple models to improve prediction performance.
- See how to embed a trained machine learning model into a web application using the chapter 9 example.