The construction of academic system with Python for you exactly how to find the sample through the raw data. The book starts by brushing up on your knowledge of Python ML and Gallery showcases, and then move on to more serious projects on data sets, models, proposals and recommendations to improve through examples and canoeing through sound and image processing details. Using open source tools and libraries, the reader will learn how to apply these methods to the text, images, and sounds. You will also learn how to evaluate, compare and select engineering school
Table of Contents
Chapter 1. Getting Started with Python Machine Learning Chapter 2. Learning How to Classify with Real-world Examples Chapter 3. Clustering – Finding Related Posts Chapter 4. Topic Modeling Chapter 5. Classification – Detecting Poor Answers Chapter 6. Classification II – Sentiment Analysis Chapter 7. Regression – Recommendations Chapter 8. Regression – Recommendations Improved Chapter 9. Classification III – Music Genre Classification Chapter 10. Computer Vision – Pattern Recognition Chapter 11. Dimensionality Reduction Chapter 12. Big(ger) Data
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