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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

Understanding models that are interpretable by design

In Chapter 7, Choosing the Best AI Algorithm, we mentioned that more complex algorithm types, such as neural networks, are often used even though they provide very little benefit. You should favor keeping it simple as much as possible, following the KISS principle (keep it simple, stupid). Not only may other models be simpler and easier to interpret, but they provide some fantastic results as well. Simple doesn't mean inferior.

We have looked at many models in this book that come with the ability to understand how the results were achieved without any special techniques. The algorithms we will cover now are as follows:

  • Decision trees
  • Linear/logistic regression
  • KNN

We'll use a medical example, as mentioned earlier in the chapter. This dataset is a binary classifier for whether someone is at risk of heart disease or not. For this chapter, we'll keep the data preparation and other steps out...

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