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- Herausgeber: Packt Publishing
- Kategorie: Wissenschaft und neue Technologien
- Sprache: Englisch
Solve challenging data science problems by mastering cutting-edge machine learning techniques in Python
About This Book
- Resolve complex machine learning problems and explore deep learning
- Learn to use Python code for implementing a range of machine learning algorithms and techniques
- A practical tutorial that tackles real-world computing problems through a rigorous and effective approach
Who This Book Is For
This title is for Python developers and analysts or data scientists who are looking to add to their existing skills by accessing some of the most powerful recent trends in data science. If you've ever considered building your own image or text-tagging solution, or of entering a Kaggle contest for instance, this book is for you!
Prior experience of Python and grounding in some of the core concepts of machine learning would be helpful.
What You Will Learn
- Compete with top data scientists by gaining a practical and theoretical understanding of cutting-edge deep learning algorithms
- Apply your new found skills to solve real problems, through clearly-explained code for every technique and test
- Automate large sets of complex data and overcome time-consuming practical challenges
- Improve the accuracy of models and your existing input data using powerful feature engineering techniques
- Use multiple learning techniques together to improve the consistency of results
- Understand the hidden structure of datasets using a range of unsupervised techniques
- Gain insight into how the experts solve challenging data problems with an effective, iterative, and validation-focused approach
- Improve the effectiveness of your deep learning models further by using powerful ensembling techniques to strap multiple models together
In Detail
Designed to take you on a guided tour of the most relevant and powerful machine learning techniques in use today by top data scientists, this book is just what you need to push your Python algorithms to maximum potential. Clear examples and detailed code samples demonstrate deep learning techniques, semi-supervised learning, and more - all whilst working with real-world applications that include image, music, text, and financial data.
The machine learning techniques covered in this book are at the forefront of commercial practice. They are applicable now for the first time in contexts such as image recognition, NLP and web search, computational creativity, and commercial/financial data modeling. Deep Learning algorithms and ensembles of models are in use by data scientists at top tech and digital companies, but the skills needed to apply them successfully, while in high demand, are still scarce.
This book is designed to take the reader on a guided tour of the most relevant and powerful machine learning techniques. Clear descriptions of how techniques work and detailed code examples demonstrate deep learning techniques, semi-supervised learning and more, in real world applications. We will also learn about NumPy and Theano.
By this end of this book, you will learn a set of advanced Machine Learning techniques and acquire a broad set of powerful skills in the area of feature selection & feature engineering.
Style and approach
This book focuses on clarifying the theory and code behind complex algorithms to make them practical, useable, and well-understood. Each topic is described with real-world applications, providing both broad contextual coverage and detailed guidance.
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Seitenzahl: 364
Veröffentlichungsjahr: 2016
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Table of Contents
Advanced Machine Learning with Python
Advanced Machine Learning with Python
Copyright © 2016 Packt Publishing
All rights reserved. No part of this book may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, without the prior written permission of the publisher, except in the case of brief quotations embedded in critical articles or reviews.
Every effort has been made in the preparation of this book to ensure the accuracy of the information presented. However, the information contained in this book is sold without warranty, either express or implied. Neither the author, nor Packt Publishing, and its dealers and distributors will be held liable for any damages caused or alleged to be caused directly or indirectly by this book.
Packt Publishing has endeavored to provide trademark information about all of the companies and products mentioned in this book by the appropriate use of capitals. However, Packt Publishing cannot guarantee the accuracy of this information.
First published: July 2016
Production reference: 1220716
Published by Packt Publishing Ltd.
Livery Place
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Birmingham B3 2PB, UK.
ISBN 978-1-78439-863-7
www.packtpub.com
Credits
Author
John Hearty
Reviewers
Jared Huffman
Ashwin Pajankar
Commissioning Editor
Akram Hussain
Acquisition Editor
Sonali Vernekar
Content Development Editor
Mayur Pawanikar
Technical Editor
Suwarna Patil
Copy Editor
Tasneem Fatehi
Project Coordinator
Nidhi Joshi
Proofreader
Safis Editing
Indexer
Mariammal Chettiyar
Graphics
Disha Haria
Production Coordinator
Arvindkumar Gupta
Cover Work
Arvindkumar Gupta
About the Author
John Hearty is a consultant in digital industries with substantial expertise in data science and infrastructure engineering. Having started out in mobile gaming, he was drawn to the challenge of AAA console analytics.
Keen to start putting advanced machine learning techniques into practice, he signed on with Microsoft to develop player modelling capabilities and big data infrastructure at an Xbox studio. His team made significant strides in engineering and data science that were replicated across Microsoft Studios. Some of the more rewarding initiatives he led included player skill modelling in asymmetrical games, and the creation of player segmentation models for individualized game experiences.
Eventually John struck out on his own as a consultant offering comprehensive infrastructure and analytics solutions for international client teams seeking new insights or data-driven capabilities. His favourite current engagement involves creating predictive models and quantifying the importance of user connections for a popular social network.
After years spent working with data, John is largely unable to stop asking questions. In his own time, he routinely builds ML solutions in Python to fulfil a broad set of personal interests. These include a novel variant on the StyleNet computational creativity algorithm and solutions for algo-trading and geolocation-based recommendation. He currently lives in the UK.
About the Reviewers
Jared Huffman is a lifelong gamer and extreme data geek. After completing his bachelor's degree in computer science, he started his career in his hometown of Melbourne, Florida. While there, he honed his software development skills, including work on a credit card-processing system and a variety of web tools. He finished it off with a fun contract working at NASA's Kennedy Space Center before migrating to his current home in the Seattle area.
Diving head first into the world of data, he took up a role working on Microsoft's internal finance tools and reporting systems. Feeling that he could no longer resist his love for video games, he joined the Xbox division to build their Business. To date, Jared has helped ship and support 12 games and presented at several events on various machine learning and other data topics. His latest endeavor has him applying both his software skills and analytics expertise in leading the data science efforts for Minecraft. There he gets to apply machine learning techniques, trying out fun and impactful projects, such as customer segmentation models, churn prediction, and recommendation systems.
Outside of work, Jared spends much of his free time playing board games and video games with his family and friends, as well as dabbling in occasional game development.
First I'd like to give a big thanks to John for giving me the honor of reviewing this book; it's been a great learning experience. Second, thanks to my amazing wife, Kalen, for allowing me to repeatedly skip chores to work on it. Last, and certainly not least, I'd like to thank God for providing me the opportunities to work on things I love and still make a living doing it. Being able to wake up every day and create games that bring joy to millions of players is truly a pleasure.
Ashwin Pajankar is a software professional and IoT enthusiast with more than 8 years of experience in software design, development, testing, and automation.
He graduated from IIIT Hyderabad, earning an M. Tech in computer science and engineering. He holds multiple professional certifications from Oracle, IBM, Teradata, and ISTQB in development, databases, and testing. He has won several awards in college through outreach initiatives, at work for technical achievements, and community service through corporate social responsibility programs.
He was introduced to Raspberry Pi while organizing a hackathon at his workplace, and has been hooked on Pi ever since. He writes plenty of code in C, Bash, Python, and Java on his cluster of Pis. He's already authored two books on Raspberry Pi and reviewed three other titles related to Python for Packt Publishing.
His LinkedIn Profile is https://in.linkedin.com/in/ashwinpajankar.
I would like to thank my wife, Kavitha, for the motivation.
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Of the many people I feel gratitude towards, I particularly want to thank my parents … mostly for their patience. I'd like to extend thanks to Tyler Lowe for his invaluable friendship, to Mark Huntley for his bothersome emphasis on accuracy, and to the former team at Lionhead Studios. I also greatly value the excellent work done by Jared Huffman and the industrious editorial team at Packt Publishing, who were hugely positive and supportive throughout the creation of this book.
Finally, I'd like to dedicate the work and words herein to you, the reader. There has never been a better time to get to grips with the subjects of this book; the world is stuffed with new opportunities that can be seized using creativity and an appropriate model. I hope for your every success in the pursuit of those solutions.
Preface
Hello! Welcome to this guide to advanced machine learning using Python. It's possible that you've picked this up with some initial interest, but aren't quite sure what to expect. In a nutshell, there has never been a more exciting time to learn and use machine learning techniques, and working in the field is only getting more rewarding. If you want to get up-to-speed with some of the more advanced data modeling techniques and gain experience using them to solve challenging problems, this is a good book for you!
What is advanced machine learning?
Ongoing advances in computational power (per Moore's Law) have begun to make machine learning, once mostly a research discipline, more viable in commercial contexts. This has caused an explosion of new applications and new or rediscovered techniques, catapulting the obscure concepts of data science, AI, and machine learning into the public consciousness and strategic planning of companies internationally.
The rapid development of machine learning applications is fueled by an ongoing struggle to continually innovate, playing out at an array of research labs. The techniques developed by these pioneers are seeding new application areas and experiencing growing public awareness. While some of the innovations sought in AI and applied machine learning are still elusively far from readiness, others are a reality. Self-driving cars, sophisticated image recognition and altering capability, ever-greater strides in genetics research, and perhaps most pervasively of all, increasingly tailored content in our digital stores, e-mail inboxes, and online lives.
With all of these possibilities and more at the fingertips of the committed data scientist, the profession is seeing a meteoric, if clumsy, growth. Not only are there far more data scientists and AI practitioners now than there were even two years ago (in early 2014), but the accessibility and openness around solutions at the high end of machine learning research has increased.
Research teams at Google and Facebook began to share more and more of their architecture, languages, models, and tools in the hope of seeing them applied and improved on by the growing data scientist population.
The machine learning community matured enough to begin seeing trends as popular algorithms were defined or rediscovered. To put this more accurately, pre-existing trends from a mainly research community began to receive great attention from industry, with one product being a group of machine learning experts straddling industry and academia. Another product, the subject of this section, is a growing awareness of advanced algorithms that can be used to crack the frontier problems of the current day. From month to month, we see new advances made, scores rise, and the frontier moves ever further out.
What all of this means is that there may never have been a better time to move into the field of data science and develop your machine learning skillset. The introductory algorithms (including clustering, regression models, and neural network architectures) and tools are widely covered in web courses and blog content. While the techniques at the cutting edge of data science (including deep learning, semi-supervised algorithms, and ensembles) remain less accessible, the techniques themselves are now available through software libraries in multiple languages. All that's needed is the combination of theoretical knowledge and practical guidance to implement models correctly. That is the requirement that this book was written to address.
What should you expect from this book?
You've begun to read a book that focuses on teaching some of the advanced modeling techniques that've emerged in recent years. This book is aimed at anyone who wants to learn about those algorithms, whether you're an experienced data scientist or developer looking to parlay existing skills into a new environment.
I aimed first and foremost at making sure that you understand the algorithms in question. Some of them are fairly tricky and tie into other concepts in statistics and machine learning.
For neophyte readers, I definitely recommend gathering an initial understanding of key concepts, including the following:
At times, this book won't be able to give a subject the attention that it deserves. We cover a lot of ground in this book and the pace is fairly brisk as a result! At the end of each chapter, I refer you to further reading, in a book or online article, so that you can build a broader base of relevant knowledge. I'd suggest that it's worth doing additional reading around any unfamiliar concept that comes up as you work through this book, as machine learning knowledge tends to tie together synergistically; the more you have, the more readily you'll understand new concepts as you expand your toolkit.
This concept of expanding a toolkit of skills is fundamental to what I've tried to achieve with this book. Each chapter introduces one or multiple algorithms and looks to achieve several goals:
Beyond the transfer of knowledge and practical skills, this book looks to achieve a more important goal; specifically, to discuss and convey some of the qualities that are common to skilled machine learning practitioners. These include creativity, demonstrated both in the definition of sophisticated architectures and problem-specific cleaning techniques. Rigor is another key quality, emphasized throughout this book by a focus on measuring performance against meaningful targets and critically assessing early efforts.
Finally, this book makes no effort to obscure the realities of working on solving data challenges: the mixed results of early trials, large iteration counts, and frequent impasses. Yet at the same time, using a mixture of toy examples, dissection of expert approaches and, toward the end of the book, more real-world challenges, we show how a creative, tenacious, and rigorous approach can break down these barriers and deliver meaningful results.
As we proceed, I wish you the best of luck and encourage you to enjoy yourself as you go, tackling the content prepared for you and applying what you've learned to new domains or data.
Let's get started!
What this book covers
Chapter 1, Unsupervised Machine Learning, shows you how to apply unsupervised learning techniques to identify patterns and structure within datasets.
Chapter 2, Deep Belief Networks, explains how the RBM and DBN algorithms work; you'll know how to use them and will feel confident in your ability to improve the quality of the results that you get out of them.
Chapter 3, Stacked Denoising Autoencoders, continues to build our skill with deep architectures by applying stacked denoising autoencoders to learn feature representations for high-dimensional input data.
Chapter 4, Convolutional Neural Networks, shows you how to apply the convolutional neural network (or Convnet).
Chapter 5, Semi-Supervised Learning, explains how to apply several semi-supervised learning techniques, including CPLE, self-learning, and S3VM.
Chapter 6, Text Feature Engineering, discusses data preparation skills that significantly increase the effectiveness of all the models that we've previously discussed.
Chapter 7, Feature Engineering Part II, shows you how to interrogate the data to weed out or mitigate quality issues, transform it into forms that are conducive to machine learning, and creatively enhance that data.
Chapter 8, Ensemble Methods, looks at building more sophisticated model ensembles and methods of building robustness into your model solutions.
Chapter 9, Additional Python Machine Learning Tools, reviews some of the best in recent tools available to data scientists, identifies the benefits that they offer, and discusses how to apply them alongside tools and techniques discussed earlier in this book, within a consistent working process.
Appendix A, Chapter Code Requirements, discusses tool requirements for the book, identifying required libraries for each chapter.
What you need for this book
The entirety of this book's content leverages openly available data and code, including open source Python libraries and frameworks. While each chapter's example code is accompanied by a README file documenting all the libraries required to run the code provided in that chapter's accompanying scripts, the content of these files is collated here for your convenience.
It is recommended that some libraries required for earlier chapters be available when working with code from any later chapter. These requirements are identified using bold text. Particularly, it is important to set up the first chapter's required libraries for any content later in the book.
Who this book is for
This title is for Python developers and analysts or data scientists who are looking to add to their existing skills by accessing some of the most powerful recent trends in data science. If you've ever considered building your own image or text-tagging solution or entering a Kaggle contest, for instance, this book is for you!
Prior experience of Python and grounding in some of the core concepts of machine learning would be helpful.
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