Master Machine Learning with In-Depth Mathematical Insights, Lecture notes of Computer Science

This comprehensive guide covers essential machine learning algorithms with a focus on the math that powers them. Topics include Simple and Multiple Linear Regression, Ridge/Lasso/Elastic Net Regression, Gradient Descent, Decision Trees, Ensemble Techniques, Random Forest, KNN, K-Means Clustering, Hierarchical Clustering, DBSCAN, and Support Vector Machines. With step-by-step breakdowns and real-world examples, this guide provides everything you need to confidently apply these algorithms and elevate your machine learning skills.