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
1. Thinking in Machine Learning
2. Tools and Techniques
3. Turning Data into Information
4. Models – Learning from Information
5. Linear Models
6. Neural Networks
7. Features – How Algorithms See the World
8. Learning with Ensembles
9. Design Strategies and Case Studies
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