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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

Feature engineering in practice


Depending on the modeling technique that you're using, some of this work may be more valuable than other parts. Deep learning algorithms tend to perform better on less-engineered data than shallower models and it might be that less work is needed to improve results.

The key to understanding what is needed is to iterate quickly through the whole process from dataset acquisition to modeling. On a first pass with a clear target for model accuracy, find the acceptable minimum amount of processing and perform that. Learn whatever you can about the results and make a plan for the next iteration.

To show how this looks in practice, we'll work with an unfamiliar, high-dimensional dataset, using an iterative process to generate increasingly effective modeling.

I was recently living in Vancouver. While it has many positive qualities, one of the worst things about living in the city was the somewhat unpredictable commute. Whether I was traveling by car or taking Translink...

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Advanced Machine Learning with Python
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