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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

Dealing with too much data

It's true that more data is usually better, but this isn't always the case. There are many times when having extra data has a negative impact on an outcome. Such a case was covered in Chapter 1, Understanding the AI/ML Landscape, where a father gave his child an extra example of what a tiger was, but that extra example was actually of a panther. That additional bit of information would then turn into a negative addition to the training set and create a worse learning outcome for your model.

How are you supposed to know this? Understand the data. This will be a common theme in this chapter, the book, and in the real world. If you don't start there, then everything else is more challenging. It's similar to being able to understand bias, as discussed in Chapter 6, Overcoming Bias in AI/ML.

Sometimes though, you won't or can't have a full grasp of the data, but you can use tools to help you out. The first clue that you can...

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