Comprehensive Beginner’s Guide to Jupyter Notebooks for Data Science & Machine Learning


Jupyter Notebooks allow data scientists to create and share their documents, from codes to full blown reports. They help data scientists streamline their work and enable more productivity and easy collaboration. Due to these and several other reasons you will see below, Jupyter Notebooks are one of the most popular tools among data scientists.

In this article, we will introduce you to Jupyter notebooks and deep dive into it’s features and advantages.

By the time you reach the end of the article, you will have a good idea as to why you should leverage it for your machine learning projects and why Jupyter Notebooks are considered better than other standard tools in this domain!

What is a Jupyter Notebook?

Jupyter Notebook is an open-source web application that allows us to create and share codes and documents.

It provides an environment, where you can document your code, run it, look at the outcome, visualize data and see the results without leaving the environment. This makes it a handy tool for performing end to end data science workflows – data cleaning, statistical modeling, building and training machine learning models, visualizing data, and many, many other uses.

Jupyter Notebooks really shine when you are still in the prototyping phase. This is because your code is written in indepedent cells, which are executed individually. 

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