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Designing Machine Learning Systems with Python

Designing Machine Learning Systems with Python

By : David Julian
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Designing Machine Learning Systems with Python

Designing Machine Learning Systems with Python

2 (1)
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 (11 chapters)
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1. Thinking in Machine Learning
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Index

Real-world case studies


Now, we will move on to some real-world machine learning scenarios. First, we will build a recommender system, and then we will look into some integrated pest management systems in greenhouses.

Building a recommender system

Recommender systems are a type of information filtering, and there are two general approaches: content-based filtering and collaborative filtering. In content-based filtering, the system attempts to model a user's long term interests and select items based on this. On the other hand, collaborative filtering chooses items based on the correlation with items chosen by people with similar preferences. As you would expect, many systems use a hybrid of these two approaches.

Content-based filtering

Content-based filtering uses the content of items, which is represented as a set of descriptor terms, and matches them with a user profile. A user profile is constructed using the same terms extracted from items that the user has previously viewed. A typical online...

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