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14-11-2021, 10:54

Jupyter Notebook for Beginners

Category: Tutorials

Jupyter Notebook for Beginners
Jupyter Notebook for Beginners
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English | Duration: 11 lectures (33 mins) | Size: 178 MB

Start working with it Quickly

What you'll learn

Work with the interactive, and powerful tool - Jupyter Notebook
Create your own notebooks on Python, Julia, R and other supported languages
Run code snippets and view the results of more than 40 languages in the notebooks
Create and visualize graphs,plots, and charts in the notebook
Work faster on notebooks using keyboard shortcuts


Access to a working computer
Knowledge of Python is an added advantage


This is an introductory course for absolute beginners who want to start with Jupyter notebook as a tool.

Who need Jupyter Notebook ?

1. If you want a highly lucrative career in machine learning, data science, deep learning, or in any other data driven technologies, you should know how to work with Jupyter Notebook: one of the most important tools a machine learning engineer or a data scientist needs in her (his) day-to-day job!

2. Jupyter Notebook is also used among scientific communities and mathematicians.

3. If you are a Python or R developer, looking for a tool to quickly run the ad-hoc scripts, see the result then and there, and fix your code as you go, Jupyter Notebook is a tool you need.

Advantages of Jupyter Notebook

It is similar to a physical notebook you carry to take notes, in the sense that - you can write textual content, embed images, videos, and run all the code and programs, see the results of your programs - all within the notebook itself. Jupyter notebook supports more than 40 programming languages. If you are learning Python, Julia or R, its highly recommended you start using Jupyter Notebook - you'll love it!

You can share and distribute the notebooks you create using Jupyter notebook as a tool.

Who this course is for:

All who want to use Jupyter Notebooks
Python programmers
Data Scientists and Data Analysts
Machine Learning Engineers


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