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 preceding chapters, we've spent time assessing powerful techniques that enable the analysis of complex or challenging data. However, for the most difficult problems, the right technique will only get you so far.

The persistent challenge that deep learning and supervised learning try to solve for is that finding solutions often requires multiple big investments from the team in question. Under the old paradigm, one often has to perform specific preparation tasks, requiring time, specialist skills, and knowledge. Often, even the techniques used were domain-specific and/or data type-specific. This process, via which features are derived, is referred to as feature engineering.

Most of the deep learning algorithms which we've studied so far are intended to help find ways around needing to perform extensive feature engineering. However, at the same time, feature engineering continues to be seen as a hugely important skill for top-level ML practitioners. The following quotes come...