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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

Summary

JupyterLab and NumPy are for data scientists what a hacksaw and nail gun are for carpenters. Is using those two things by themselves carpentry? Not exactly, but they are vital tools that you will need in order to be able to achieve the work you want to. This is the same for data science – JupyterLab and NumPy don't cover everything, but they are two things that are going to play an important role in what you are trying to get done.

In this chapter, we discovered how to launch Jupyter notebooks from Anaconda Navigator and how to easily break down work into small chunks and evaluate the parts bit by bit. We saw that you can use a bit of line and cell magic to perform some special actions such as timing a function or operations. We also looked at some ways to speed up operations to save you valuable time. Finally, we saw how execution order matters and that you can use that as a powerful tool to explore.

We also looked at how NumPy can basically be used as a...

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