- Laptop with internet connection
- No prior Programming or Data Experience required
| # | Module | Topics | Playlist |
|---|---|---|---|
| 01 | Intro to AI & Data Science | AI basics, ML concepts, data science lifecycle | YouTube |
| 02 | Python Foundations | Data types, OOP, functions, file handling | YouTube |
| 03 | Python Projects | Guessing game, APIs, web scraping projects | YouTube |
| 04 | Python Web Scraping | BeautifulSoup, scraping techniques | YouTube |
| 05 | Git & GitHub | Version control, collaboration | YouTube |
| 06 | Python Data Analysis | Linear algebra, statistics, EDA | YouTube |
| 07 | Streamlit | App building, deployment | YouTube |
| 08 | Data Preprocessing & Feature Engineering | Cleaning, scaling, encoding, pipelines | YouTube |
| 09 | Python Machine Learning | Regression, classification, clustering, PCA | YouTube |
Total: 147 videos | ~23 hours
Each module folder contains:
- README.md — Topic table with video links, PDFs, and code
- PDFs/ — Theory notes and slides (where available)
- CODE/ — Jupyter notebooks and project code (where available)
Navigate to any module, find a topic, and access the video, notes, and code directly from the table.
Q: Is this course free? A: Yes. All videos, materials, and code are free and open-source.
Q: Do I need prior ML experience? A: No. The course starts from the basics. No prior programming or data experience required.
Q: Can I take it self-paced? A: Yes. Follow the materials on GitHub at your own pace.
Q: How do I get help? A: Open an issue on GitHub or reach out via the YouTube comments.
This project is licensed under the MIT License — see LICENSE for details.