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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

Defining bias versus discrimination

Let's start by making sure we have a clear understanding of the two components in the context of AI – bias and discrimination. There are different aspects to each of these components and it's important to understand the difference between them.

Bias in AI/ML

AI/ML bias is when models that have been created show favor toward certain groups or categories that doesn't reflect the actual state of the world.

Bias is inevitable in any model and in itself can be harmless. Let's say you are going to author a paper about the most popular foods and do some analysis on them. To do so, you collect data from your friends and family as to their preferences. In addition to this, think about the three foods that you would reply with. Are there any vegetables in there? Any Ethiopian foods? Anything from Turkey? Perhaps not.

This is a form of bias; unless you take a perfectly even sample size of people across the world, you are...

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