Machine learning

Learn what NLP is, how tokenization and transformers work, and where natural language processing shows up in real products today.

Understand support vector machines from margin maximization to kernel tricks, with scikit-learn examples, strengths, weaknesses, and when to use SVMs.

Learn what XGBoost is, how gradient boosting works, and when to choose it over neural networks for structured datasets.

How to run k-Nearest Neighbors (KNN) over a merged, multi-query Google SERP corpus to find hidden semantic connections.

Turning climbing strength metrics into grade predictions using scikit-learn, FastAPI, React, and Databricks MLflow. Deployed with Docker + Railway