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

Building intelligent machines to transform data into knowledge

In this age of modern technology, there is one resource that we have in abundance: a large amount of structured and unstructured data. In the second half of the twentieth century, machine learning evolved as a subfield of artificial intelligence that involved the development of self-learning algorithms to gain knowledge from that data in order to make predictions. Instead of requiring humans to manually derive rules and build models from analyzing large amounts of data, machine learning offers a more efficient alternative for capturing the knowledge in data to gradually improve the performance of predictive models, and make data-driven decisions. Not only is machine learning becoming increasingly important in computer science research but it also plays an ever greater role in our everyday life. Thanks to machine learning, we enjoy robust e-mail spam filters, convenient text and voice recognition software, reliable Web search engines, challenging chess players, and, hopefully soon, safe and efficient self-driving cars.