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

Chapter 9. Design Strategies and Case Studies

With the possible exception of data munging, evaluating is probably what machine learning scientists spend most of their time doing. Staring at lists of numbers and graphs, watching hopefully as their models run, and trying earnestly to make sense of their output. Evaluation is a cyclical process; we run models, evaluate the results, and plug in new parameters, each time hoping that this will result in a performance gain. Our work becomes more enjoyable and productive as we increase the efficiency of each evaluation cycle, and there are some tools and techniques that can help us achieve this. This chapter will introduce some of these through the following topics:

  • Evaluating model performance

  • Model selection

  • Real-world case studies.

  • Machine learning design at a glance