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.
Code used:
https://github.com/campusx-official/running-kaggle-dataset-colab-for-deep-learning
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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