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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

Classification

Being able to put things into certain classes might be the most common type of ML application that you see in the world, and has been a staple of the industry for a long time.

There are two main types of classification: binary classification and multi-class classification. As the names indicate, binary classification is when the outcome only has two possible options. It's very common to have a true or false outcome in this setup.

Multi-class classification is when there are more than two possible classes. This could be for a variety of scenarios, such as movie genre. The approaches taken for them are very similar to a binary classification problem.

Let's check out some examples that might help you get a better grasp on problems that fall into the classification bucket:

Whether emails are spam or not (binary)

  • Whether you would survive the Titanic sinking (binary)
  • Identifying the type of flower (multi-class)
  • Labeling handwritten...
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