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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

Creating and evaluating regression algorithms

We talked about a few different algorithms in previous chapters. Which one should we choose? There are pros and cons for each, and sometimes it's not apparent which one we should go for. In this section, we'll look at a few possible algorithms and do a quick check to determine how viable each of them is. We'll then train the winner and finally analyze in more depth the results by looking at a few evaluation techniques. Before we do that, let's make sure we are looking at the correct problem family.

Comparing regression and classification

When we've looked at the target data and what our goal is, we saw that the quality is measured by discrete values from 1 to 9. If that's the case, then why aren't we looking at this as a classification problem? The short answer is we could. This example was chosen to make you think about the nuances that can arise with data science, and the answer you get depends...

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