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Machine Learning With Go

You're reading from   Machine Learning With Go Implement Regression, Classification, Clustering, Time-series Models, Neural Networks, and More using the Go Programming Language

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Product type Paperback
Published in Sep 2017
Publisher Packt
ISBN-13 9781785882104
Length 304 pages
Edition 1st Edition
Languages
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Author (1):
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Joseph Langstaff Whitenack Joseph Langstaff Whitenack
Author Profile Icon Joseph Langstaff Whitenack
Joseph Langstaff Whitenack
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Table of Contents (11) Chapters Close

Preface 1. Gathering and Organizing Data 2. Matrices, Probability, and Statistics FREE CHAPTER 3. Evaluation and Validation 4. Regression 5. Classification 6. Clustering 7. Time Series and Anomaly Detection 8. Neural Networks and Deep Learning 9. Deploying and Distributing Analyses and Models 10. Algorithms/Techniques Related to Machine Learning

Summary

Choosing an appropriate evaluation metric and laying out a procedure for evaluation/validation are essential parts of any machine learning project. You have learned about a variety of relevant evaluation metrics and how to avoid overfitting using holdout sets and/or cross validation. In the next chapter, we will start looking at machine learning models and we will build our first model using linear regression!

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