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  • Book Overview & Buying Designing Machine Learning Systems with Python
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Designing Machine Learning Systems with Python

Designing Machine Learning Systems with Python

By : David Julian, Vahid Mirjalili
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Designing Machine Learning Systems with Python

Designing Machine Learning Systems with Python

2 (1)
By: David Julian, Vahid Mirjalili

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 (11 chapters)
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1. Thinking in Machine Learning
10
Index

Principle component analysis


Principle Component Analysis (PCA) is the most common form of dimensionality reduction that we can apply to features. Consider the example of a dataset consisting of two features and we would like to convert this two-dimensional data into one dimension. A natural approach would be to draw a line of the closest fit and project each data point onto this line, as shown in the following graph:

PCA attempts to find a surface to project the data by minimizing the distance between the data points and the line we are attempting to project this data to. For the more general case where we have n dimensions and we want to reduce this space to k-dimensions, we find k vectors u(1),u(2), ..., u(k) onto which to project the data so as to minimize the projection error. That is we are trying to find a k-dimensional surface to project the data.

This looks superficially like linear regression however it is different in several important ways. With linear regression we are trying...

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