Book Image

Advanced Machine Learning with Python

Book Image

Advanced Machine Learning with Python

Overview of this book

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.

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Advanced Machine Learning with Python
Jul, 2016 | 278 pages
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Table of Contents (17 chapters)
Advanced Machine Learning with Python
Advanced Machine Learning with Python
Credits
Credits
About the Author
About the Author
About the Reviewers
About the Reviewers
www.PacktPub.com
www.PacktPub.com
Preface
Preface
Free Chapter
1
Unsupervised Machine Learning
Unsupervised Machine Learning
Principal component analysis
Introducing k-means clustering
Self-organizing maps
Further reading
Summary
2
Deep Belief Networks
Deep Belief Networks
Neural networks – a primer
Restricted Boltzmann Machine
Deep belief networks
Further reading
Summary
3
Stacked Denoising Autoencoders
Stacked Denoising Autoencoders
Autoencoders
Stacked Denoising Autoencoders
Further reading
Summary
4
Convolutional Neural Networks
Convolutional Neural Networks
Introducing the CNN
Further Reading
Summary
5
Semi-Supervised Learning
Semi-Supervised Learning
Introduction
Understanding semi-supervised learning
Semi-supervised algorithms in action
Further reading
Summary
6
Text Feature Engineering
Text Feature Engineering
Introduction
Text feature engineering
Further reading
Summary
7
Feature Engineering Part II
Feature Engineering Part II
Introduction
Creating a feature set
Feature engineering in practice
Further reading
Summary
8
Ensemble Methods
Ensemble Methods
Introducing ensembles
Using models in dynamic applications
Further reading
Summary
9
Additional Python Machine Learning Tools
Additional Python Machine Learning Tools
Alternative development tools
Further reading
Summary
Chapter Code Requirements
Chapter Code Requirements
Index
Index

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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