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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

Anomaly detection

If you've ever gotten a text saying that your bank has noticed some suspicious activity, chances are they have put anomaly detection to use. Anomaly detection is the attempt to determine whether an event, item, or object doesn't fit in with the others. One of these things is not like the other is a good way to think about it. Another name you might see for this is outlier detection.

You will find unsupervised, supervised, and semi-supervised approaches can all work in these scenarios. A depiction of what this looks like can be found in Figure 1.4 of Chapter 1, Understanding the AI/ML Landscape.

Many of the examples in this space handle more serious issues around security and safety. You'll find some examples in the following list:

  • Credit card fraud
  • If someone is trying to hack your account via random logins
  • Unsafe operations at a power plant
  • Customer buying patterns
  • Illegal trading activity on a stock

There are...

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