Machine Learning Prerequisites: Git and Anaconda

To become a professional coder, one of the most basic requirements is to be comfortable with Git, GitHub and Anaconda Navigator. These have become the quintessential resources for developers to make better software.
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Just how google drive, google docs and google sheets have become the most used resources for collaborative work for both personal and professional use, Git and Github are primarily used for codes.

Git and GitHub 

Let’s start with the basics of Git and GitHub.

We can directly understand from the makers, what Git and GitHub is all about from the Git Handbook.

To understand how to start with basic operations on Git, we look at an edureka blog post on how to use GitHub. This post provides you with the details about basic operations in GitHub, like adding a file, forking and cloning. It provides a detailed explanation about GitHub to beginners.

Next, we learn how to use Git locally in your system and push it to the cloud after making changes in this Git and GitHub tutorial for beginners. You will get a more detailed understanding on working with Git on your personal computer and how to make changes and push it back to Git.

Git and the list of Git commands provide the developer with a lot of flexibility. To dive into the details of Git commands and how to use them, this will be a useful resource.

Even after this, if you still feel the need to gain more information on Git and GitHub, watch this video for tutorials and to develop an enhanced understanding about the service.


One of the biggest resource centers on how to work with Git, is by its team itself, find it here.

Anaconda Navigator

Let's start with how to install anaconda navigator.

To understand the basic operations of anaconda navigator and getting familiar with the software, the best resources are the user guides provided by creators themselves:

To understand how to use Anaconda for varied use cases, in this Python Anaconda Tutorial.

Like this article? Learn about other machine learning prerequisites - linear algebra, probability, calculus.

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