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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

You might have heard the saying garbage in, garbage out when it comes to data and AI. In this chapter, you saw that we should also take just as seriously the phrase bias in, bias out.

We looked at some of the primary areas where bias can creep into our data and saw that we must start with an eye toward finding this sooner rather than later. At certain points in the process, it's too late. Bias and discrimination can have real-world impacts, from hiring and vehicle safety to continuing unjust practices around social norms.

You have a few options to make sure that you are doing all you can to avoid this bias such as having the domain knowledge or consulting those who do and getting others from different backgrounds to look at data (or better yet, on your team).

There are also many other types of bias that exist out there and, admittingly, things that we don't even realize are areas of concern. It's also important to be aware that the drift talked about...

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