Resources
Books
- FastBook
- Python Data Science Handbook - By Jake VanderPlas
- Python for Data Analysis - By Wes McKinney | Book to learn about data handling with standard ML Libraries
Guides
- https://www.interviewbit.com/system-design-interview-questions/amp/
- https://www.promptingguide.ai/ Prompt Engineering
- https://medium.com/marvelous-mlops/mlops-roadmap-2024-ff4216b8bc62 Mooc
- The Missing Semester of Your CS Education (mit.edu) MIT Missing CS Semester + Lectures: Teaches how to use system utility tools like git, vim, other useful untaught methods for Machine Learning.
- https://hepsoftwarefoundation.org/training/center.html Multiple Advance Tutorial/Guide Series. Recommended
Lectures
- Cornell Machine Learning for Intelligent Systems 2018 36 Lectures and different Assignments https://www.youtube.com/watch?v=MrLPzBxG95I&list=PLl8OlHZGYOQ7bkVbuRthEsaLr7bONzbXS
References
- Commands : List of Command Line Commands | Codecademy
- Bash vs Windows : Learn the Command Line | Codecademy
- Virtual Environment : Python: Modules | Codecademy
- Statistics Cheat Sheet : https://terenceshin.medium.com/week-2-52-stats-cheat-sheet-ae38a2e5cdc6
- Statistics in Maths: Definition, Types, Formulas, and Applications (geeksforgeeks.org)
- APIs : https://github.com/TwilioDevEd/introduction-to-apis-notes/blob/main/course-notes.md
- How web works : https://github.com/TwilioDevEd/introduction-to-apis-notes/blob/main/course-notes.md
Repositories
Good for Beginners
- Matlab + Python | Guided GitHub - radekosmulski/10_neural_nets
- https://www.instagram.com/p/C1W4QlzLBER/?igsh=MWFma29oM21qandnYg==
- https://diegoinacio.github.io/machine-learning-notebooks-page/