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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

Who this book is for

This book is for anyone that not only wants to better understand the world of data science but also those that have a decent grasp and want to become more well rounded in their knowledge on things such as Anaconda tools and Open Source Software (OSS). Assume that you don't have a grasp of areas such as bias or interpretability and that you still don't know all the various types of algorithms you can use to create AI/ML models. We've designed this book to be as self-contained as possible, so you'll only need outside resources when you want to go deeper.

Some basic technical knowledge is expected, but being a developer or even knowing much about data science is not a necessity. You can read this book from beginning to end, or you can jump to the chapters that seem most relevant to you. While each chapter does build on the previous ones, we have structured it in such a way that you won't be lost if you choose to navigate to a specific topic.

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