My implementations for building intuition in ML
This repo documents my hands-on journey into the world of machine learning through coding up algorithms, techniques, and concepts across disciplines like:
Regression
Classification
Clustering
Deep Learning
NLP
Dimensionality Reduction
Boosting
Model Selection
Reinforcement Learning
Primary source is the Udemy course "Machine Learning A-Z" but I incorporate learnings from other materials as well.
Each section features Jupyter notebooks delving into libraries like Scikit-Learn, Keras, PyTorch, and key papers. Through annotations and documentation, I aim to map out the intracies of both seminal and cutting-edge ML.
This will evolve over time into an integrated resource for picking up practical ML experience. Feedback is welcome!