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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

Evaluating and using scikit-learn

Let's say you want to tackle a problem you have, such as wanting to evaluate the price of houses in California. You know you want to evaluate the popular data science framework scikit-learn to see if it fits your need. Scikit-learn is a very powerful ready-to-go solution that allows you to train and evaluate many different types of models.

One of the most powerful things a library or piece of software can do is provide abstraction so that you can do a lot with a little. Scikit-learn exposes just enough that with very readable and specific commands the result is the creation of a model that years ago would have taken you a week to come up with on your own.

Think of it as being like giving your order to a waiter. You give them a high-level insight into what you would like to eat, and they relay that information to the chef who already knows how to prepare a fantastic meal. You don't need to worry about what exact temperature to set the...

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