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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

Technical requirements

There are a few things that you will need to get the most out of this chapter. They are as follows:

  • Anaconda Distribution. This includes conda and Navigator.You can download that from the following URL: https://www.anaconda.com/products/distribution
  • A conda environment with scikit-learn, pandas, and matplotlib.
  • A Jupyter notebook to perform all the coding segments. You also can use any IDE of choice or even the command line, but the assumption is that you will be working in a notebook.

After you have that set up, we can look at our first topic – how to deal with having extra data.

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