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.

Related Content you might be interested in

Current Title:

Small book image
Advanced Machine Learning with Python
Jul, 2016 | 278 pages
No titles found
Table of Contents (17 chapters)
Advanced Machine Learning with Python
Advanced Machine Learning with Python
Credits
Credits
About the Author
About the Author
About the Reviewers
About the Reviewers
www.PacktPub.com
www.PacktPub.com
Preface
Preface
Free Chapter
1
Unsupervised Machine Learning
Unsupervised Machine Learning
Principal component analysis
Introducing k-means clustering
Self-organizing maps
Further reading
Summary
2
Deep Belief Networks
Deep Belief Networks
Neural networks – a primer
Restricted Boltzmann Machine
Deep belief networks
Further reading
Summary
3
Stacked Denoising Autoencoders
Stacked Denoising Autoencoders
Autoencoders
Stacked Denoising Autoencoders
Further reading
Summary
4
Convolutional Neural Networks
Convolutional Neural Networks
Introducing the CNN
Further Reading
Summary
5
Semi-Supervised Learning
Semi-Supervised Learning
Introduction
Understanding semi-supervised learning
Semi-supervised algorithms in action
Further reading
Summary
6
Text Feature Engineering
Text Feature Engineering
Introduction
Text feature engineering
Further reading
Summary
7
Feature Engineering Part II
Feature Engineering Part II
Introduction
Creating a feature set
Feature engineering in practice
Further reading
Summary
8
Ensemble Methods
Ensemble Methods
Introducing ensembles
Using models in dynamic applications
Further reading
Summary
9
Additional Python Machine Learning Tools
Additional Python Machine Learning Tools
Alternative development tools
Further reading
Summary
Chapter Code Requirements
Chapter Code Requirements
Index
Index

Further reading

Victor Powell and Lewis Lehe provide a fantastic interactive, visual explanation of PCA at http://setosa.io/ev/principal-component-analysis/, 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 http://arxiv.org/abs/1404.1100.

For a thorough worked example that translates Jonathon's description into clear Python code, consider Sebastian Raschka's demonstration using the Iris dataset at http://sebastianraschka.com/Articles/2015_pca_in_3_steps.html.

Finally, consider the sklearn documentation for more details on arguments to the PCA class at http://scikit-learn.org/stable/modules/generated/sklearn.decomposition.PCA.html.

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 http://varianceexplained.org/r/kmeans-free-lunch/.

A specific discussion of the Elbow method is provided by Rick Gove at https://bl.ocks.org/rpgove/0060ff3b656618e9136b.

Finally, consider sklearn's documentation for another view on unsupervised learning algorithms, including k-means at http://scikit-learn.org/stable/tutorial/statistical_inference/unsupervised_learning.html.

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 http://www.cs.bham.ac.uk/~jxb/NN/l16.pdf.

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 https://onlinecourses.science.psu.edu/stat501/node/344.

Previous Section
End of Section 5
Next Section