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

Using models in dynamic applications


We've spent this chapter discussing the use of techniques to manage model performance under conditions that might be seen as ideal; specifically, conditions in which all of the data is available ahead of time so that a model can be trained on all data. These assumptions are frequently valid in research contexts or when dealing with one-time problems, but in many contexts they are unsafe assumptions. The range of unsafe contexts goes beyond the cases where the data is simply unavailable, such as data science contests where a held-out dataset is used to establish the final leaderboard.

Returning to a subject from earlier in this chapter, you'll recall the Pragmatic Chaos algorithm, which won the Netflix prize? By the time Netflix came to assessing the algorithm for implementation, both the business context and requirements had shifted so dramatically that the minimal accuracy gains provided by that algorithm didn't justify implementation costs. The $1M algorithm...