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

Introduction


In previous chapters, we've tackled a range of data challenges using advanced techniques. In each case, we've applied our techniques to datasets with reasonable success.

In many regards, though, we've had it pretty easy. Our data has been largely derived from canonical and well-prepared sources so we haven't had to do a great deal of preparation. In the real world, though, there are few datasets like this (except, perhaps, the ones that we're able to specify ourselves!). In particular, it is rare and improbable to come across a dataset in the wild, which has class labels available. Without labels on a sufficient portion of the dataset, we find ourselves unable to build a classifier that can accurately predict labels on validation or test data. So, what do we do?

The common solution is attempt to tag our data manually; not only is this time-consuming, but it also suffers from certain types of human error (which are especially common with high-dimensional datasets, where a human...