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


We've been introduced to a lot of useful and highly applicable skills in this chapter. In this chapter, we took a set of messy, complication-strewn text data and, through a series of rigorous steps, turned it into a large set of effective features. We began by picking up a set of data cleaning skills which stripped out a lot of the noise and problem elements, then we followed up by turning text into features using POS tagging and bag of words. In the process, you learned to apply a set of techniques that are widely applicable and very empowering, enabling us to solve difficult problems in many natural language processing contexts.

Through experimentation with multiple individual models and ensembles, we discovered that where a smarter algorithm might not yield a strong result, thorough and creative feature engineering can yield massive improvements in model performance.