Название книги: Building Machine Learning Systems with Python Second Edition
Год: 2015
Автор: Луис Педро Коэльо, Вилли Ричард
Язык: Английский
Формат: pdf
Размер: 5.9 МВ

Описание книги «Building Machine Learning
Systems with Python Second Edition»: 

One could argue that it is a fortunate coincidence that you are holding this book in
your hands (or have it on your eBook reader). After all, there are millions of books
printed every year, which are read by millions of readers. And then there is this book read by you. One could also argue that a couple of machine learning algorithms
played their role in leading you to this book—or this book to you. And we, the authors, are happy that you want to understand more about the hows and whys. Most of the book will cover the how. How has data to be processed so that machine learning algorithms can make the most out of it? How should one choose the right
algorithm for a problem at hand?

Occasionally, we will also cover the why. Why is it important to measure correctly?
Why does one algorithm outperform another one in a given scenario? We know that there is much more to learn to be an expert in the field. After all, we only covered some howsand just a tiny fraction of the whys. But in the end, we hope that this mixture will help you to get up and running as quickly as possible.


This book is for Python programmers who want to learn how to perform machine
learning using open source libraries. We will walk through the basic modes of machine learning based on realistic examples.

This book is also for machine learners who want to start using Python to build their
systems. Python is a flexible language for rapid prototyping, while the underlying algorithms are all written in optimized C or C++. Thus the resulting code is fast and
robust enough to be used in production as well.

  1. Getting Started with Python Machine Learning
  2. Classifying with Real-world Examples
  3. Clustering – Finding Related Posts
  4. Topic Modeling
  5. Classification – Detecting Poor Answers
  6. Classification II – Sentiment Analysis
  7. Regression
  8. Recommendations
  9. Classification – Music Genre Classification
  10. Computer Vision
  11. Dimensionality Reduction
  12. Bigger Data

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