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You're reading from Designing Machine Learning Systems with Python

Product type Book

Published in Apr 2016

Publisher

ISBN-13 9781785882951

Pages 232 pages

Edition 1st Edition

Languages

Python

Concepts

Machine Learning

Author (1):

David Julian

Table of Contents (16) Chapters

Designing Machine Learning Systems with Python

Credits

About the Author

About the Reviewer

www.PacktPub.com

Preface

1. Thinking in Machine Learning

2. Tools and Techniques

3. Turning Data into Information

4. Models – Learning from Information

5. Linear Models

6. Neural Networks

7. Features – How Algorithms See the World

8. Learning with Ensembles

9. Design Strategies and Case Studies

Index

SciPy

SciPy (pronounced sigh pi) adds a layer to NumPy that wraps common scientific and statistical applications on top of the more purely mathematical constructs of NumPy. SciPy provides higher-level functions for manipulating and visualizing data, and it is especially useful when using Python interactively. SciPy is organized into sub-packages covering different scientific computing applications. A list of the packages most relevant to ML and their functions appear as follows:

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Authors (1)

David Julian

David Julian is a freelance technology consultant and educator. He has worked as a consultant for government, private, and community organizations on a variety of projects, including using machine learning to detect insect outbreaks in controlled agricultural environments (Urban Ecological Systems Ltd., Bluesmart Farms), designing and implementing event management data systems (Sustainable Industry Expo, Lismore City Council), and designing multimedia interactive installations (Adelaide University). He has also written Designing Machine Learning Systems With Python for Packt Publishing and was a technical reviewer for Python Machine Learning and Hands-On Data Structures and Algorithms with Python - Second Edition, published by Packt.

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Package	Description
`cluster`	This contains two sub-packages: `cluster.vq` for K-means clustering and vector quantization. `cluster.hierachy` for hierarchical and agglomerative clustering, which is useful for distance matrices, calculating statistics on clusters, as well as visualizing clusters with dendrograms.
`constants`	These are physical and mathematical constants such as pi and e.
`integrate`	These are differential equation solvers
`interpolate`	These are interpolation functions for creating new data...