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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
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Index

Logical models


Logical models divide the instance space, that is the set of all possible or allowable, instances, into segments. The goal is to ensure that the data in each segment is homogeneous with respect to a particular task. For example, if the task is classification, then we aim to ensure that each segment contains a majority of instances of the same class.

Logical models use logical expressions to explain a particular concept. The simplest and most general logical expressions are literals, and the most common of these is equality. The equality expression can be applied to all types—nominative, numerical, and ordinal. For numerical and ordinal types, we can include the inequality literals: greater than or less than. From here, we can build more complex expressions using four logical connectives. These are conjunction (logical AND), which is denoted by ; disjunction (logical OR), which is denoted by ; implication, which is denoted by ; and negation, which is denoted by . This provides...

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