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Building Data Science Solutions with Anaconda

Building Data Science Solutions with Anaconda

By : Meador
5 (12)
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Building Data Science Solutions with Anaconda

Building Data Science Solutions with Anaconda

5 (12)
By: Meador

Overview of this book

You might already know that there's a wealth of data science and machine learning resources available on the market, but what you might not know is how much is left out by most of these AI resources. This book not only covers everything you need to know about algorithm families but also ensures that you become an expert in everything, from the critical aspects of avoiding bias in data to model interpretability, which have now become must-have skills. In this book, you'll learn how using Anaconda as the easy button, can give you a complete view of the capabilities of tools such as conda, which includes how to specify new channels to pull in any package you want as well as discovering new open source tools at your disposal. You’ll also get a clear picture of how to evaluate which model to train and identify when they have become unusable due to drift. Finally, you’ll learn about the powerful yet simple techniques that you can use to explain how your model works. By the end of this book, you’ll feel confident using conda and Anaconda Navigator to manage dependencies and gain a thorough understanding of the end-to-end data science workflow.
Table of Contents (16 chapters)
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1
Part 1: The Data Science Landscape – Open Source to the Rescue
6
Part 2: Data Is the New Oil, Models Are the New Refineries
11
Part 3: Practical Examples and Applications

Summary

In this chapter, we covered a lot of ground. We looked at what it means to be open source by digging into how the OSI defines it as a common understanding that the source code should be accessible, open to change, and not limited by industry, among other things.

We found out about the major licenses that you'll come across on your journey and the differences between them. You saw that copyleft licenses such as GPL require you to share anything you create, but permissive licenses give you permission to keep those things for yourself, like MIT licenses do.

We then looked at the criteria that you can use to evaluate whether an open source tool might be for you by using things such as the number of GitHub stars, the number of maintainers, and how long it's been around. Looking at some of these things holistically lets us put together a better picture of whether we can count on our OSS tool to be maintained and reliable.

Finally, we saw how you can access the...

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