You're reading from Python Machine Learning Learn how to build powerful Python machine learning algorithms to generate useful data insights with this data analysis tutorial

Product type Paperback

Published in Sep 2015

Publisher Packt

ISBN-13 9781783555130

Length 454 pages

Edition 1st Edition

Languages

Python

Tools

SciPy

Concepts

Machine Learning

Author (1):

Sebastian Raschka

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Table of Contents (15) Chapters

Preface

1. Giving Computers the Ability to Learn from Data

2. Training Machine Learning Algorithms for Classification FREE CHAPTER

3. A Tour of Machine Learning Classifiers Using Scikit-learn

4. Building Good Training Sets – Data Preprocessing

5. Compressing Data via Dimensionality Reduction

6. Learning Best Practices for Model Evaluation and Hyperparameter Tuning

7. Combining Different Models for Ensemble Learning

8. Applying Machine Learning to Sentiment Analysis

9. Embedding a Machine Learning Model into a Web Application

10. Predicting Continuous Target Variables with Regression Analysis

11. Working with Unlabeled Data – Clustering Analysis

12. Training Artificial Neural Networks for Image Recognition

13. Parallelizing Neural Network Training with Theano

Index

What you need for this book

The execution of the code examples provided in this book requires an installation of Python 3.4.3 or newer on Mac OS X, Linux, or Microsoft Windows. We will make frequent use of Python's essential libraries for scientific computing throughout this book, including SciPy, NumPy, scikit-learn, matplotlib, and pandas.

The first chapter will provide you with instructions and useful tips to set up your Python environment and these core libraries. We will add additional libraries to our repertoire and installation instructions are provided in the respective chapters: the NLTK library for natural language processing (Chapter 8, Applying Machine Learning to Sentiment Analysis), the Flask web framework (Chapter 9, Embedding a Machine Learning Algorithm into a Web Application), the seaborn library for statistical data visualization (Chapter 10, Predicting Continuous Target Variables with Regression Analysis), and Theano for efficient neural network training on graphical processing units (Chapter 13, Parallelizing Neural Network Training with Theano).