ml-foundations

The foundations of ML, built from scratch — one notebook at a time. NumPy → Pandas → Visualization → Statistics → ML → Deep Learning.

Foundational machine learning coursework and implementation exercises. Documenting core concepts, algorithm logic, and practical applications developed during the TurkstudentCo ML program.

Stepping into the AI/ML universe might feel like trying to tame a dragon, but with this slick roadmap, you’ll be slaying it like a pro in no time. We’re talking a step-by-step glow-up from newbie to ML wizard, and all you need is this dope YouTube playlists to light the way. Ready to level up? Let’s roll!

A structured ML foundations sprint covering linear algebra, eigen/SVD/PCA derivations, statistical learning, and engineered ML pipelines.

A structured collection of notes covering the mathematics, classical algorithms, and neural-network concepts that form the foundations of machine learning.

A Machine Learning foundation project demonstrating end-to-end data preprocessing, feature engineering, exploratory data analysis, and data preparation techniques using an insurance dataset before model development.