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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

Data acquisition

In the earlier exercises, I asked you to look at the dots and figure out the distance. This gives a hint as to how we need to think of our data. We need to think of our data as coordinates in some imaginary coordinate space. Now, our data won't be just two-dimensional, because it's textual. Instead, it'll be multidimensional. This gives us hints as to how our data will look—slices of numbers representing a coordinate in some arbitrarily large N-dimensional space.

But, first, we'll need to get the data.

To acquire the tweets from the feed, we'll be using Aditya Mukherjee's excellent Anaconda library. To install it, simply run go get -u github.com/ChimeraCoder/Anaconda.

Of course, one can't just grab data from Twitter willy-nilly. We will need to acquire data via the Twitter API. The documentation of Twitter's API is...

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