Installing Anaconda For Data Science | Jupyter Notebook for Machine Learning | Google Colab for ML

Anaconda is a distribution of the Python and R programming languages for scientific computing (data science, machine learning applications, large-scale data processing, predictive analytics, etc.), that aims to simplify package management and deployment. The distribution includes data-science packages suitable for Windows, Linux, and macOS. It is developed and maintained by Anaconda.

The Jupyter Notebook is an open-source web application that allows you to create and share documents that contain live code, equations, visualizations and narrative text. Uses include: data cleaning and transformation, numerical simulation, statistical modeling, data visualization, machine learning, and much more.

A virtual environment is a tool that helps to keep dependencies required by different projects separate by creating isolated Python virtual environments for them. This is one of the most important tools that most Python developers use.

Kaggle allows users to find and publish data sets, explore and build models in a web-based data-science environment, work with other data scientists and machine learning engineers, and enter competitions to solve data science challenges.

Collaboratory, or “Collab” for short, is a product from Google Research. Collab allows anybody to write and execute arbitrary python code through the browser and is especially well suited to machine learning, data analysis, and education.

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⌚Time Stamps⌚

00:00 - Intro
01:30 - Installing Anaconda
03:00 - Spyder
03:50 - Jupyter Notebook
11:34 - Virtual Environment
23:40 - Using Kaggle
29:20 - Google Collab
36:28 - Outro

[ritugujela]

Sir, I'm glad I came across to your YouTube channel. It's literally a blessing to all Data Science aspirants. Following your ML playlist + explanation is brilliant. Tons of Thanks to you !!!! You deserve a lot more subscribers :)

[aayushrawat]

i was scratching my head whether i should use colab or anaconda and this guy made it so easy.... thanks

[paragvachhani]

Sir aapne to economical weaker section ke aspirant ke problem ka solution de diya..u r such a great teacher...

[creativevision]

lot of us indian students will be well versed and experienced in machine learning thanks to you.

[ShashwatPandeyindia]

This is a very very helpful video .... especially the last kaggle dataset to google colab part. Thanks a lot Nitish !

[Taraprasaddash]

Feeling blessed to come across your channel

[manuradha]

You are simply superb. Your videos are basic and easy to follow through. Thank you for doing this to your community.

[xjlqoxl]

I still feel its a blessing to get to know about your channel

[vipin]

Very informative, also pls mk video on how to upload test data and how its done for
kaggle competition

[arpitajana]

Thankk you soo much I wish koi hume college mein aise sikhata, I really struggled with importing Datasets before 🙃

[amancoder]

till now what i feel is that it is best playlist, hope i enjoy this whole playlist, thanks a lot sir, for such a effort

[The_big_shot]

thank you sir . Just got the best playlist in the whole youtube

[hasanrants]

Thank you Sir for the playlist. You're a great teacher.
Love from Pakistan, 5/7/2024

[Tech_Charla]

Excellent way to explain Anaconda environment. Thank you

[ashishprasadverma]

Thankyou so much sir, now I have learned how to use Kaggle data in collab.

[ckxunwq]

33:58 important, how to use kaggle dataset directly in colab

[practicaltechyoutubechanne]

Thank you much sir. I bought a new mac for machine learning and I was struggling to set up my required software. You vedio helped a loot.

[muhammadhabib]

You are a jam sir. I must say that you are my life Saviour

[fit_tubes_]

Course Started : ML
Lecture-01: 14/08/2024
Lecture-02: 14/08/2024
Lecture-03: 14/08/2024
Lecture-04: 14/08/2024
Lecture-05: 14/08/2024
Lecture-06: 15/08/2024
Lecture-07: 15/08/2024
Lecture-08: 15/08/2024
Lecture-09: 15/08/2024
Lecture-10: 15/08/2024
Lecture-11: 16/08/2024
Lecture-12: 16/08/2024

[Scooterboy_and_others]

Fantastic Demo, Thanks for sharing the tips & tricks