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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

Chapter 2: Analyzing Open Source Software

You can't have a grasp of data science unless you understand open source. It is the oxygen that has fueled the explosion of artificial intelligence (AI) growth in the last two decades. You will be hard-pressed to find any software product or tool being used today that does not make use of open source or is not open source itself.

In this chapter, we will learn what it means for a tool to be open source and how that limits (or does not) how you can use it. We will then walk through how to find and start using different open source tools in your projects today. Finally, we will put these skills to use by evaluating and using one of the most popular open source tools for data science, scikit-learn.

We will focus on the following topics:

  • Understanding open source
  • Understanding the top four OSS licenses
  • Evaluating a new tool or library
  • Importing packages using the Anaconda distibution and conda-forge
  • Evaluating...
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