Python Machine Learning
2nd edition, published September 6th 2017

Paperback: 501? pages
Publisher: Packt Publishing
Language: English

ISBN-10: 1787125939
ISBN-13: 978-1787125933
Kindle ASIN: B0742K7HYF

• Amazon Page
• Packt Page

Helpful installation and setup instructions can be found in the README.md file code/ subdirectory

Simply click on the open dir links next to the chapter headlines to navigate to the chapter subdirectories located in the code/ subdirectory. You can also click on the ipynb links below to open and view the Jupyter notebook of each chapter directly on GitHub.

Please note that these are just the code examples accompanying the book, which I uploaded for your convenience; be aware that these notebooks may not be useful without the formulae and descriptive text.

1. Machine Learning - Giving Computers the Ability to Learn from Data [open dir] [ipynb]
2. Training Machine Learning Algorithms for Classification [open dir] [ipynb]
3. A Tour of Machine Learning Classifiers Using Scikit-Learn [open dir] [ipynb]
4. Building Good Training Sets – Data Pre-Processing [open dir] [ipynb]
5. Compressing Data via Dimensionality Reduction [open dir] [ipynb]
6. Learning Best Practices for Model Evaluation and Hyperparameter Optimization [open dir]
7. Combining Different Models for Ensemble Learning [open dir] [ipynb]
8. Applying Machine Learning to Sentiment Analysis [open dir] [ipynb]
9. Embedding a Machine Learning Model into a Web Application [open dir] [ipynb]
10. Predicting Continuous Target Variables with Regression Analysis [open dir] [ipynb]
11. Working with Unlabeled Data – Clustering Analysis [open dir] [ipynb]
12. Implementing a Multi-layer Artificial Neural Network from Scratch [open dir] [ipynb]
13. Parallelizing Neural Network Training with TensorFlow
14. Going Deeper: The Mechanics of TensorFlow
15. Classifying Images with Deep Convolutional Neural Networks [open dir] [ipynb]
16. Modeling Sequential Data Using Recurrent Neural Networks