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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

Overcoming exclusion bias

Exclusions bias is when you choose to delete information that isn't considered useful. One of the strengths of AI is that it can find patterns or relationships for things that you didn't realize existed. This can happen more often if individuals or a team don't have a good set of domain knowledge around a subject and therefore are dismissive of items that they don't realize would be valuable.

An added danger arises if data scientists believe that they know an area well enough to be able to create models around it. This can go hand in hand with the Dunning–Kruger effect, which is a potential cognitive bias where people with low skill in a particular area overestimate their ability. You don't know what you don't know, meaning that when you are new to an area, there are many aspects of it you can't even realize are gaps in your knowledge. Conversely, you can have people with high knowledge in an area perceiving their...

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