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

Operations and statistics


Features can be defined by the allowable operations that can be performed on them. Consider two features: a person's age and their phone number. Although both these features can be described by integers, they actually represent two very different types of information. This is clear when we see which operations we can usefully perform on them. For example, calculating the average age of a group of people will give us a meaningful result; calculating the average phone number will not.

We can call the range of possible calculations that can be performed on a feature as its statistics. These statistics describe three separate aspects of data. These are—its central tendency, its dispersion, and its shape.

To calculate the central tendency of data, we usually use one or more of the following statistics: the mean (or average), the median (or the middle value in an ordered list), and the mode (or the majority of all values). The mode is the only statistic that can be applied...