Machine Learning Course for Beginners

Beginner-level video course covering the fundamentals of machine learning: supervised and unsupervised learning, linear and logistic regression, regularization, support vector machines, principal component analysis, learning theory, decision trees, ensemble methods including boosting and stacking, K-means, and hierarchical clustering. Concepts are applied in hands-on projects: a house price predictor, a stock price predictor, heart failure prediction, and a spam/ham detector. Suited to newcomers who want a broad first pass over classic ML algorithms.

Syllabus

Course Introduction.
Fundamentals of Machine Learning.
Supervised Learning and Unsupervised Learning In Depth.
Linear Regression.
Logistic Regression.
Project: House Price Predictor.
Regularization.
Support Vector Machines.
Project: Stock Price Predictor.
Principal Component Analysis.
Learning Theory.
Decision Trees.
Ensemble Learning.
Boosting, pt 1.
Boosting, pt 2.
Stacking Ensemble Learning.
Unsupervised Learning, pt 1.
Unsupervised Learning, pt 2.
K-Means.
Hierarchical Clustering.
Project: Heart Failure Prediction.
Project: Spam/Ham Detector.