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Building Data Science Solutions with Anaconda

You're reading from  Building Data Science Solutions with Anaconda

Product type Book
Published in May 2022
Publisher Packt
ISBN-13 9781800568785
Pages 330 pages
Edition 1st Edition
Languages
Author (1):
Dan Meador Dan Meador
Profile icon Dan Meador

Table of Contents (16) Chapters

Preface Part 1: The Data Science Landscape – Open Source to the Rescue
Chapter 1: Understanding the AI/ML landscape Chapter 2: Analyzing Open Source Software Chapter 3: Using the Anaconda Distribution to Manage Packages Chapter 4: Working with Jupyter Notebooks and NumPy Part 2: Data Is the New Oil, Models Are the New Refineries
Chapter 5: Cleaning and Visualizing Data Chapter 6: Overcoming Bias in AI/ML Chapter 7: Choosing the Best AI Algorithm Chapter 8: Dealing with Common Data Problems Part 3: Practical Examples and Applications
Chapter 9: Building a Regression Model with scikit-learn Chapter 10: Explainable AI - Using LIME and SHAP Chapter 11: Tuning Hyperparameters and Versioning Your Model Other Books You May Enjoy

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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