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Building Data Science Solutions with Anaconda

You're reading from   Building Data Science Solutions with Anaconda A comprehensive starter guide to building robust and complete models

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Product type Paperback
Published in May 2022
Publisher Packt
ISBN-13 9781800568785
Length 330 pages
Edition 1st Edition
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Author (1):
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Dan Meador Dan Meador
Author Profile Icon Dan Meador
Dan Meador
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Table of Contents (16) Chapters Close

Preface 1. Part 1: The Data Science Landscape – Open Source to the Rescue
2. Chapter 1: Understanding the AI/ML landscape FREE CHAPTER 3. Chapter 2: Analyzing Open Source Software 4. Chapter 3: Using the Anaconda Distribution to Manage Packages 5. Chapter 4: Working with Jupyter Notebooks and NumPy 6. Part 2: Data Is the New Oil, Models Are the New Refineries
7. Chapter 5: Cleaning and Visualizing Data 8. Chapter 6: Overcoming Bias in AI/ML 9. Chapter 7: Choosing the Best AI Algorithm 10. Chapter 8: Dealing with Common Data Problems 11. Part 3: Practical Examples and Applications
12. Chapter 9: Building a Regression Model with scikit-learn 13. Chapter 10: Explainable AI - Using LIME and SHAP 14. Chapter 11: Tuning Hyperparameters and Versioning Your Model 15. Other Books You May Enjoy

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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