Machine Learning

17 lectures

Core supervised and unsupervised learning algorithms, model selection, and evaluation.

01Lecture 01 · Introduction to Machine Learning — Session 1

02Lecture 02 · Data Preprocessing for Machine Learning — Lecture 2

03Lecture 03 · Feature Engineering and Linear Regression

04Lecture 04 · Linear Regression — Complete Lecture Notes

05Lecture 05 · Gradient Descent, Regularization, and Logistic Regression

06Lecture 06 · Regularization — Worked Numerical Examples

07Lecture 07 · Logistic Regression — Complete Enriched Lecture Notes

08Lecture 08 · Decision Trees

09Lecture 09 · Decision Trees — Overfitting, Pruning, and MDL

10Lecture 10 · Instance-Based Learning and K-Nearest Neighbors

11Lecture 11 · Instance-Based Learning — Distance-Weighted KNN, Locally Weighted Regression, and Bayesian Learning Foundations

12Lecture 12 · Bayesian Learning — Maximum Likelihood, MAP, and Naive Bayes

13Lecture 13 · Naive Bayes Classifier and Ensemble Learning

14Lecture 14 · Ensemble Methods — Bagging, Random Forest, and Boosting

15Lecture 15 · Ensemble Learning, Gradient Boosting, and Introduction to Unsupervised Learning

16Lecture 16 · Gaussian Mixture Models and Support Vector Machines

17Lecture 17 · Support Vector Machines — Soft Margin, Non-Linear SVM, and Model Fairness

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