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  • Book Overview & Buying Advanced Machine Learning with Python
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Advanced Machine Learning with Python

Advanced Machine Learning with Python

3.1 (7)
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Advanced Machine Learning with Python

Advanced Machine Learning with Python

3.1 (7)

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 (12 chapters)
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10
A. Chapter Code Requirements
11
Index

Chapter 8. Ensemble Methods

As we progressed through the earlier chapters of this book, you learned how to apply a number of new techniques. We developed our use of several advanced machine learning algorithms and acquired a broad range of companion techniques used to enhance your use of learning techniques via more effective feature selection and preparation. This chapter seeks to enhance your existing technique set using ensemble methods: techniques that bind multiple different models together to solve a real-world problem.

Ensemble techniques have become a fundamental part of the data scientist's toolset. The use of ensembles has become common practice in competitive machine learning contexts, and ensembles are now considered an indispensable tool in many contexts. The techniques that we'll develop in this chapter give our models an edge in performance, while increasing their robustness to underlying data change.

We'll examine a series of ensembling options, discussing...

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Advanced Machine Learning with Python
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