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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

Every situation and dataset you see will be unique; however, the problems you encounter with them won't be. In this chapter, you saw issues that will come up repeatedly with the datasets you'll be working with.

We saw how having too much data can be a problem by having highly correlated features, and how you can find that correlation and remove it. We used the example of college recruiting points and rank, but you can easily find others in the real world, such as housing prices – you might have the price per square footage but also have those as separate features.

Working with categorical data is common, but at the end of the day, machine learning models need numbers to be able to work. We saw that there are times when we want to keep relationships between categorical values, such as a rating system, and other times when we don't. We saw how we can use one-hot encoding to encode these categories when we don't want to keep the relationships.

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