What you need for this book
by Willi Richert, Luis Pedro Coelho
Building Machine Learning Systems with Python
- Building Machine Learning Systems with Python
- Table of Contents
- Building Machine Learning Systems with Python
- Credits
- About the Authors
- About the Reviewers
- www.PacktPub.com
- Preface
- 1. Getting Started with Python Machine Learning
- Machine learning and Python – the dream team
- What the book will teach you (and what it will not)
- What to do when you are stuck
- Getting started
- Our first (tiny) machine learning application
- Summary
- 2. Learning How to Classify with Real-world Examples
- 3. Clustering – Finding Related Posts
- 4. Topic Modeling
- 5. Classification – Detecting Poor Answers
- 6. Classification II – Sentiment Analysis
- 7. Regression – Recommendations
- 8. Regression – Recommendations Improved
- 9. Classification III – Music Genre Classification
- 10. Computer Vision – Pattern Recognition
- 11. Dimensionality Reduction
- 12. Big(ger) Data
- A. Where to Learn More about Machine Learning
- Index
This book assumes you know Python and how to install a library using easy_install or pip. We do not rely on any advanced mathematics such as calculus or matrix algebra.
To summarize it, we are using the following versions throughout this book, but you should be fine with any more recent one:
- Python: 2.7
- NumPy: 1.6.2
- SciPy: 0.11
- Scikit-learn: 0.13
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