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Go Machine Learning Projects

You're reading from   Go Machine Learning Projects Eight projects demonstrating end-to-end machine learning and predictive analytics applications in Go

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Product type Paperback
Published in Nov 2018
Publisher Packt
ISBN-13 9781788993401
Length 348 pages
Edition 1st Edition
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Author (1):
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Xuanyi Chew Xuanyi Chew
Author Profile Icon Xuanyi Chew
Xuanyi Chew
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Table of Contents (12) Chapters Close

Preface 1. How to Solve All Machine Learning Problems 2. Linear Regression - House Price Prediction FREE CHAPTER 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

Decomposition

There are two things to note about the previous screenshot:

  • CO2 levels in the air are steadily rising over time.
  • There are dips and then bumps in the levels of CO2, but the result still ends up rising overall. These dips and bumps happen on a regular pattern.

The first point is what is known to statisticians as a trend. You may already be familiar with the notion of a Trend Line from Microsoft Excel. A trend is a kind of pattern that describes gradual change over time. In our case, it is quite clear that the trend is upward.

The second point is called seasonality—for very apt reasons, as it may turn out. Seasonality describes the pattern of variance that happens regularly. If you carefully look at the chart, typically at around August to October of each year, the CO2 levels drop to the lowest point of the year. After which, they rise steadily again until...

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