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
About the Author
About the Reviewers
Chapter Code Requirements

Further reading

Victor Powell and Lewis Lehe provide a fantastic interactive, visual explanation of PCA at, this is ideal for readers who are new to the core concepts of PCA or who are not quite getting it.

For a lengthier and more mathematically-involved treatment of PCA, touching on underlying matrix transformations, Jonathon Shlens from Google research provides a clear and thorough explanation at

For a thorough worked example that translates Jonathon's description into clear Python code, consider Sebastian Raschka's demonstration using the Iris dataset at

Finally, consider the sklearn documentation for more details on arguments to the PCA class at

For a lively and expert treatment of k-means, including detailed investigations of the conditions that cause it to fail, and potential alternatives in such cases, consider David Robinson's fantastic blog, variance explained at

A specific discussion of the Elbow method is provided by Rick Gove at

Finally, consider sklearn's documentation for another view on unsupervised learning algorithms, including k-means at

Much of the existing material on Kohonen's SOM is either rather old, very high-level, or formally expressed. A decent alternative to the description in this book is provided by John Bullinaria at

For readers interested in a deeper understanding of the underlying mathematics, I'd recommend reading the work of Tuevo Kohonen directly. The 2012 edition of self-organising maps is a great place to start.

The concept of multicollinearity, referenced in the chapter, is given a clear explanation for the unfamiliar at