Supervised Machine Learning: Regression and Classification
Andrew Ng opens the Machine Learning Specialization built with Stanford Online and DeepLearning.AI by teaching linear regression, logistic regression, gradient descent, and the practical mechanics of training models in Python with NumPy and scikit-learn. Three modules spread across roughly three weeks mix 20-plus short videos with practice quizzes and ungraded labs in the first week alone, then move into multiple linear regression with vectorization and feature engineering, and finish with classification, overfitting, and regularization. It updates and expands Ng's original 2012 Machine Learning course, which more than 4.8 million learners have taken and which helped launch Coursera itself. All videos and assignments are available through a free trial or full audit, with financial aid available for anyone who wants the certificate but cannot pay. Graduates come away able to build and evaluate basic supervised learning models from scratch.