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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

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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