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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
1. Thinking in Machine Learning
10
Index

Summary

In this chapter, we looked at the major ensemble methods and their implementations in scikit-learn. It is clear that there is a large space to work in and finding what techniques work best for different types of problems is the key challenge. We saw that the problems of bias and variance each have their own solution, and it is essential to understand the key indicators of each of these. Achieving good results usually involves much experimentation, and using some of the simple techniques described in this chapter, you can begin your journey into machine learning ensembles.

In the next and last chapter, we will introduce the most important topic—model selection and evaluation—and examine some real-world problems from different perspectives.

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