// the find
krishnaik06/Complete-Data-Science-With-Machine-Learning-And-NLP-2024
A dump of Jupyter notebooks and handwritten-note PDFs that accompany Krish Naik's paid Udemy course on ML/NLP with MLOps. It's course material, not a library or tool — useful if you want free-adjacent reference notebooks for classic ML algorithms (regression, trees, boosting, clustering) plus a few end-to-end deployment examples.
Genuinely broad coverage of classical ML — every major algorithm from linear regression through XGBoost, PCA, and clustering has its own folder with a working notebook and a companion PDF of the math. The MLOps section (MLflow, DVC, BentoML, CI/CD zips) is a rare inclusion in a beginner-oriented ML repo and shows actual deployment code, not just theory.
The README is 100% a sales pitch for the Udemy course with no setup instructions, no requirements.txt, and no explanation of how the folders relate to each other. The repo is structurally a mess — duplicate CSVs (Travel.csv, cardekho_imputated.csv) copied into every algorithm folder instead of a shared data directory, project code shipped as opaque zip files instead of checked-in source, and inconsistent naming/typos in folder names (Adaboost, NAive Baye's). No commits since July 2024 and no CI, tests, or license file for the code itself beyond the top-level LICENSE, so treat every notebook as a frozen snapshot, not something to build on.