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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

At this point, you have a much better grasp of how to look at a real-world problem and understand the full flow that needs to occur.

Using the backdrop of making a high-quality wine, you first saw how getting a better sense of the problem space is very important for framing what we need to do, and one way to do this was by understanding each column in our dataset.

After that, we looked at how to further explore and clean the data. You saw how the data cleaning phase can be split into two parts, with the dividing line being when things need to be human-readable, and when you need to focus on building a good model. Things such as scaling the data should happen after you feel like you've got an understanding of what you are looking at.

In the pre-training data phase, we made sure to set up a conda environment with everything we needed, including Jupyter notebooks. When we loaded it up, the first thing we did was to get our two different datasets and combine them...

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