Advanced Machine Learning with Python

Solve challenging data science problems by mastering cutting-edge machine learning techniques in Python
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Advanced Machine Learning with Python

John Hearty

3 customer reviews
Solve challenging data science problems by mastering cutting-edge machine learning techniques in Python
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Book Details

ISBN 139781784398637
Paperback278 pages

Book Description

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

Chapter 1: Unsupervised Machine Learning
Principal component analysis
Introducing k-means clustering
Self-organizing maps
Further reading
Summary
Chapter 2: Deep Belief Networks
Neural networks – a primer
Restricted Boltzmann Machine
Deep belief networks
Further reading
Summary
Chapter 3: Stacked Denoising Autoencoders
Autoencoders
Stacked Denoising Autoencoders
Further reading
Summary
Chapter 4: Convolutional Neural Networks
Introducing the CNN
Further Reading
Summary
Chapter 5: Semi-Supervised Learning
Introduction
Understanding semi-supervised learning
Semi-supervised algorithms in action
Further reading
Summary
Chapter 6: Text Feature Engineering
Introduction
Text feature engineering
Further reading
Summary
Chapter 7: Feature Engineering Part II
Introduction
Creating a feature set
Feature engineering in practice
Further reading
Summary
Chapter 8: Ensemble Methods
Introducing ensembles
Using models in dynamic applications
Further reading
Summary
Chapter 9: Additional Python Machine Learning Tools
Alternative development tools
Further reading
Summary

What You Will Learn

  • Compete with top data scientists by gaining a practical and theoretical understanding of cutting-edge deep learning algorithms
  • Apply your new found skills to solve real problems, through clearly-explained code for every technique and test
  • Automate large sets of complex data and overcome time-consuming practical challenges
  • Improve the accuracy of models and your existing input data using powerful feature engineering techniques
  • Use multiple learning techniques together to improve the consistency of results
  • Understand the hidden structure of datasets using a range of unsupervised techniques
  • Gain insight into how the experts solve challenging data problems with an effective, iterative, and validation-focused approach
  • Improve the effectiveness of your deep learning models further by using powerful ensembling techniques to strap multiple models together

Authors

Table of Contents

Chapter 1: Unsupervised Machine Learning
Principal component analysis
Introducing k-means clustering
Self-organizing maps
Further reading
Summary
Chapter 2: Deep Belief Networks
Neural networks – a primer
Restricted Boltzmann Machine
Deep belief networks
Further reading
Summary
Chapter 3: Stacked Denoising Autoencoders
Autoencoders
Stacked Denoising Autoencoders
Further reading
Summary
Chapter 4: Convolutional Neural Networks
Introducing the CNN
Further Reading
Summary
Chapter 5: Semi-Supervised Learning
Introduction
Understanding semi-supervised learning
Semi-supervised algorithms in action
Further reading
Summary
Chapter 6: Text Feature Engineering
Introduction
Text feature engineering
Further reading
Summary
Chapter 7: Feature Engineering Part II
Introduction
Creating a feature set
Feature engineering in practice
Further reading
Summary
Chapter 8: Ensemble Methods
Introducing ensembles
Using models in dynamic applications
Further reading
Summary
Chapter 9: Additional Python Machine Learning Tools
Alternative development tools
Further reading
Summary

Book Details

ISBN 139781784398637
Paperback278 pages
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