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Go Machine Learning Projects

You're reading from  Go Machine Learning Projects

Product type Book
Published in Nov 2018
Publisher Packt
ISBN-13 9781788993401
Pages 348 pages
Edition 1st Edition
Languages
Author (1):
Xuanyi Chew Xuanyi Chew
Profile icon Xuanyi Chew

Table of Contents (12) Chapters

Preface 1. How to Solve All Machine Learning Problems 2. Linear Regression - House Price Prediction 3. Classification - Spam Email Detection 4. Decomposing CO2 Trends Using Time Series Analysis 5. Clean Up Your Personal Twitter Timeline by Clustering Tweets 6. Neural Networks - MNIST Handwriting Recognition 7. Convolutional Neural Networks - MNIST Handwriting Recognition 8. Basic Facial Detection 9. Hot Dog or Not Hot Dog - Using External Services 10. What's Next? 11. Other Books You May Enjoy

What's Next?

The projects covered in this book can be considered bite-sized projects. They can be completed within a day or two. A real project will often take months. They require a combination of machine learning expertise, engineering expertise, and DevOps expertise. It would not quite be feasible to write about such projects without spanning multiple chapters while keeping the same level of detail. In fact, as can be witnessed by the progression of this book, as projects get more complex, the level of detail drops. In fact, the last two chapters are pretty thin.

All said and done, we've achieved quite a bit in this book. However, there is quite a bit we have not covered. This is owing to my own personal lack of expertise in some other fields in machine learning. In the introductory chapter, I noted that there are multiple classification schemes for machine learning...

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