A. Where to Learn More about Machine Learning
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
We are at the end of our book and now take a moment to look at what else is out there that could be useful for our readers.
There are many wonderful resources out there to learn more about machine learning (way too much to cover them all here). Our list can therefore represent only a small and very biased sampling of the resources we think are best at the time of writing.
Andrew Ng is a Professor at Stanford who runs an online course in machine learning as a massive open online course (MOOC) at Coursera (http://www.coursera.org). It is free of charge, but may represent a significant investment of time and effort (return on investment guaranteed!).
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