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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

Importing packages with Anaconda and conda-forge

This section might be one of the most valuable in the entire book as it's such a foundational part of the work you will do day to day as a data scientist (and as a developer). In any given project or even a small proof of concept, you will use many packages to accomplish what you need to, so let's look at how conda and conda-forge work together to get you what you need.

The conda package manager and Navigator are great tools, but they are useless without the packages themselves. For any given update to a package, there are things that might have changed with it or new dependencies brought in. For example, TensorFlow (https://github.com/tensorflow/tensorflow), the popular machine learning framework, is looking at releasing version 2.6.0. This release splits out a major part, Keras, so now there may be libraries that aren't needed and new ones that are. Some package updates are very minor, but some require a lot of manual...

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