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

Creating a feature set

The most important factor involved in successful machine learning is the quality of your input data. A good model with misleading, inappropriately normalized, or uninformative data will not see the same level of success anywhere near a model run over appropriately prepared data.

In some cases, you have the ability to specify data collection or have access to a useful, sizeable, and varied set of source data. With the right knowledge and skillset, you can use this data to create highly useful feature sets.

In general, having a strong knowledge as to how to construct good feature sets is very helpful as it enables you to audit and assess any new dataset for missed opportunities. In this chapter, we will introduce a design process and technique set that make it easier to create effective feature sets.

As such, we'll begin by discussing some techniques that we can use to extend or reinterpret existing features, potentially creating a large number of useful parameters to include...

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