Book Image

Designing Machine Learning Systems with Python

By : David Julian
Book Image

Designing Machine Learning Systems with Python

By: David Julian

Overview of this book

Machine learning is one of the fastest growing trends in modern computing. It has applications in a wide range of fields, including economics, the natural sciences, web development, and business modeling. In order to harness the power of these systems, it is essential that the practitioner develops a solid understanding of the underlying design principles. There are many reasons why machine learning models may not give accurate results. By looking at these systems from a design perspective, we gain a deeper understanding of the underlying algorithms and the optimisational methods that are available. This book will give you a solid foundation in the machine learning design process, and enable you to build customised machine learning models to solve unique problems. You may already know about, or have worked with, some of the off-the-shelf machine learning models for solving common problems such as spam detection or movie classification, but to begin solving more complex problems, it is important to adapt these models to your own specific needs. This book will give you this understanding and more.
Table of Contents (16 chapters)
Designing Machine Learning Systems with Python
Credits
About the Author
About the Reviewer
www.PacktPub.com
Preface
Free Chapter
1
Thinking in Machine Learning
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:

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...