To gain an understanding of which cleaning operations may be required for a particular dataset, we need to consider how the data was collected. One of the major cleaning operations involves dealing with missing data. We have already encountered an example of this in the last chapter, when we examined the temperature data. In this instance, the data had a quality parameter, so we could simply exclude the incomplete data. However, this may not be the best solution for many applications. It may be necessary to fill in the missing data. How do we decide what data to use? In the case of our temperature data, we could fill the missing values in with the average values for that time of year. Notice that we presuppose some domain knowledge, for example, the data is more or less periodic; it is in line with the seasonal cycle. So, it is a fair assumption that we could take the average for that particular date for every year we have a reliable record. However, consider that we are attempting...
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