These are working notes, written mostly to get things straight in my own head. They are not textbook chapters and I do not polish them much.
Machine Learning
An Introduction to MLOps: A Complete Tutorial with a Hands-On Jun 2026 Tree-Based Models Explained: From Decision Trees to Random Forest and XGBoost May 2026 Softmax Regression Explained from Logistic Regression Apr 2026 Gradient Descent Algorithm Explained Nov 2025
Regression (read as a series)
1 Simple Linear Regression Jan 2026 2 Multiple Linear Regression Jan 2026 3 Polynomial Regression Jan 2026 4 Correlation Jan 2026 5 R-squared: SST, SSE, SSR and the Relationship with Correlation Jan 2026 6 Standard Error in Regression Feb 2026 7 Confidence Intervals for Regression Coefficients Feb 2026 8 Statistical Testing in Regression Feb 2026
Stochastic Modelling
Signal Processing
Reinforcement Learning
Reinforcement learning practices in healthcare applications Oct 2025 Action-value methods with incremental step size in reinforcement learning Oct 2025