Sign In Start Free Trial
Account

Add to playlist

Create a Playlist

Modal Close icon
You need to login to use this feature.
  • Building Data Science Solutions with Anaconda
  • Toc
  • feedback
Building Data Science Solutions with Anaconda

Building Data Science Solutions with Anaconda

By : Meador
5 (12)
close
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)
close
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

Using NumPy to perform calculations quickly

As we talked about in the How OSS and Anaconda create the data science landscape section in Chapter 2, Analyzing Open Source Software, open source builds on itself. One library uses another to do some basic operations, and that library then itself can be used by something else in order to accomplish a different task or do the same thing in a more abstract way. NumPy is one of those base libraries that is used by a huge number of tools and frameworks to handle fast mathematical operations for arrays.

Created by Travis Oliphant (who later went on to help found Anaconda, Inc), NumPy is used by scikit-learn, SciPy, and pandas in order to focus on the respective problems they are trying to solve and lets NumPy do what it's good at. Getting a good grasp of NumPy allows you to better understand those other higher abstractions that use NumPy later on, as well as being able to use it directly when you are cleaning and creating datasets.

...
bookmark search playlist download font-size

Change the font size

margin-width

Change margin width

day-mode

Change background colour

Close icon Search
Country selected

Close icon Your notes and bookmarks

Delete Bookmark

Modal Close icon
Are you sure you want to delete it?
Cancel
Yes, Delete