Earlier in this book, I introduced the idea of the PAC learning model and the idea of concept classes. A related idea is that of weak learnability. Here each of the learning algorithms in the ensemble need only perform slightly better than chance. For example if each algorithm in the ensemble is correct at least 51% of the time then the criteria of weak learnability are satisfied. It turns out that the ideas of PAC and weak learnability are essentially the same except that for the latter, we drop the requirement that the algorithm must achieve arbitrarily high accuracy. However, it merely performs better than a random hypothesis. How is this useful, you may ask? It is often easier to find rough rules of thumb rather than a highly accurate prediction rule. This weak learning model may only perform slightly better than chance; however, if we boost this learner by running it many times on different weighted distributions of the data and by combining these learners, we can, hopefully...
Designing Machine Learning Systems with Python
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Designing Machine Learning Systems with Python
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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.
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Table of Contents (16 chapters)
Designing Machine Learning Systems with Python
Credits
About the Author
About the Reviewer
www.PacktPub.com
Preface
Free Chapter
Thinking in Machine Learning
Tools and Techniques
Turning Data into Information
Models – Learning from Information
Linear Models
Neural Networks
Features – How Algorithms See the World
Learning with Ensembles
Design Strategies and Case Studies
Index
Customer Reviews