This repository comprises of the codes and resources pertaining to python and machine learning, that I cater to students during sessions for community of Data Science enthusiasts in my campus for free to encourage them to pursue this field in their career.

Books:

• C LANGUAGE: Click here
• C++ LANGUAGE: Click here
• PYTHON LANGUAGE: Click here
• JAVA LANGUAGE: Click here
• ANDROID: Click here
• R LANGUAGE: Click here
• ALGORITHMS: Click here
• WEB DEVELOPMENT: HTML | CSS | JavaScript | JQuery | PHP | ReactJS | ReactNative | NodeJS
• DATABASE: MySQL | SQL | PostgreSQL | MongoDB
• LINUX: Click here
• GIT: Click here
• MATLAB: Click here

Roadmaps:

• Web Development: Click here
• Data Science: Click here | Click here
• Application Development: Click here [Android] | Click here [iOS] | Click here [Cross Platform]
• Software Development: Click here
• Ethical Hacking: Click here
• CyberSecurity: Click here
• Cloud Computing: Click here

Learning Tutorials:

• Web Development: Click here [Full Course] | Click here [Fundamentals] | Click here [Web Design] | Click here [MERN Stack]
• Data Science: Click here
• Application Development: Click here [Android Playlist] | Click here [Android Full Course] | Click here [iOS Playlist] | Click here | Click here [Flutter Full Course] | Click here [Flutter Playlist]
• Software Engineering: Click here [Playlist]
• Data Structures and Algorithms: Click here [Playlist] | Click here [Playlist]
• Ethical Hacking: Click here [Playlist]
• CyberSecurity: Click here [Playlist] | Click here [Full Course]
• Cloud Computing: Click here [Full Comprehensive Course] | Click here [AWS Playlist 1] | Click here [AWS Playlist 2] | Click here [GCP Playlist] | Click here [GCP Full Course] | Click here [Azure Full COurse] | Click here [Azure Playlist]

During the first two session for the community, I talked about various computer science roadmaps (including data science), explained different career pathways, and solved doubts & queires of students who didn't have any senior (mentor) to talk and clear their doubts with.

In the 3rd session, I took a hand-on approach to learning the python library for statistics called 'Numpy'. You can find the python notebook used in the session in 'Session 3' sub repository.

In the 4th session, I took a hand-on approach to learning the python library for statistics called 'Pandas'. You can find the python notebook used in the session in 'Session 4' sub repository.

In the 5th session, I took a hand-on approach to learning the python libraries for visualization called 'Matplotlib' and 'Seaborn'. You can find the python notebook used in the session in 'Session 5' sub repository.

In the 6th session, I took a hands-on approach to learn linear regression using python. You can find the python notebook used in the session in 'Session 6' sub repository.

In the 7th session, I took a hands-on approach to learn Multivariate Regression using python. You can find the python notebook used in the session in 'Session 7' sub repository.