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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

Understanding regression problems with examples

Figuring out the price of a stock, what your house should be worth, and the future temperature of the Earth all have one thing in common: they all can be thought of as regression problems. It's simply the goal of figuring out what a number would be, given a set of independent variables.

A few more examples that fall into this problem type are as follows:

  • Price of a car
  • Sales forecast for next year
  • Number of people who will sign up for a promotion

When you see a problem like this, you can try a few different models. There are many specific algorithms that you can use, each with its own pros and cons. Let's look at a few of these algorithms in the next section.

The following are a few of the most common regression algorithms you'll want to try. For each of these algorithms, we're going to take an example and create a regression model:

  • Linear regression
  • Random forest
  • Support...
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