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.
Table of Contents (17 chapters)
Advanced Machine Learning with Python
Credits
About the Author
About the Reviewers
www.PacktPub.com
Preface
Chapter Code Requirements
Index

Introducing ensembles


 

"This is how you win ML competitions: you take other peoples' work and ensemble them together."

 
 --Vitaly Kuznetsov NIPS2014

In the context of machine learning, an ensemble is a set of models that is used to solve a shared problem. An ensemble is made up of two components: a set of models and a set of decision rules that govern how the results of those models are combined into a single output.

Ensembles offer a data scientist the ability to construct multiple solutions for a given problem and then combine these into a single final result that draws from the best elements of each input solution. This provides robustness against noise, which is reflected in more effective training against an initial dataset (leading to lower levels of overfitting and reductions in training error) and against data change of the kinds discussed in the preceding section.

It is no exaggeration to say that ensembles are the most important recent development in machine learning.

In addition, ensembles...