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Python Machine Learning

You're reading from  Python Machine Learning

Product type Book
Published in Sep 2015
Publisher Packt
ISBN-13 9781783555130
Pages 454 pages
Edition 1st Edition
Languages
Author (1):
Sebastian Raschka Sebastian Raschka
Profile icon Sebastian Raschka

Table of Contents (21) Chapters

Python Machine Learning
Credits
Foreword
About the Author
About the Reviewers
www.PacktPub.com
Preface
1. Giving Computers the Ability to Learn from Data 2. Training Machine Learning Algorithms for Classification 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

Foreword

We live in the midst of a data deluge. According to recent estimates, 2.5 quintillion (1018) bytes of data are generated on a daily basis. This is so much data that over 90 percent of the information that we store nowadays was generated in the past decade alone. Unfortunately, most of this information cannot be used by humans. Either the data is beyond the means of standard analytical methods, or it is simply too vast for our limited minds to even comprehend.

Through Machine Learning, we enable computers to process, learn from, and draw actionable insights out of the otherwise impenetrable walls of big data. From the massive supercomputers that support Google's search engines to the smartphones that we carry in our pockets, we rely on Machine Learning to power most of the world around us—often, without even knowing it.

As modern pioneers in the brave new world of big data, it then behooves us to learn more about Machine Learning. What is Machine Learning and how does it work? How can I use Machine Learning to take a glimpse into the unknown, power my business, or just find out what the Internet at large thinks about my favorite movie? All of this and more will be covered in the following chapters authored by my good friend and colleague, Sebastian Raschka.

When away from taming my otherwise irascible pet dog, Sebastian has tirelessly devoted his free time to the open source Machine Learning community. Over the past several years, Sebastian has developed dozens of popular tutorials that cover topics in Machine Learning and data visualization in Python. He has also developed and contributed to several open source Python packages, several of which are now part of the core Python Machine Learning workflow.

Owing to his vast expertise in this field, I am confident that Sebastian's insights into the world of Machine Learning in Python will be invaluable to users of all experience levels. I wholeheartedly recommend this book to anyone looking to gain a broader and more practical understanding of Machine Learning.

Dr. Randal S. Olson

Artificial Intelligence and Machine Learning Researcher, University of Pennsylvania

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