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

Summary


In this chapter, you learned and applied a set of techniques that enable us to effectively build and finesse datasets for machine learning, starting from very little initial data. These powerful techniques enable a data scientist to turn seemingly shallow datasets into opportunities. We demonstrated this power using a set of customer service tweets to create a travel disruption predictor.

In order to take that solution into production, though, we'd need to add some functionality. Removing some locations in the penultimate step was a questionable decision; if this solution is intended to identify journey disruption risk, then removing locations seems like a non-starter! This is particularly true given that we do not have year-round data and so cannot identify the effect of seasonal or longitudinal trends (like extended maintenance works or a scheduled station closure). We were a little hasty in removing these elements and a better solution would be to retain them for a longer period...