Machine Learning with Python: A Practical Introduction

IBM covers supervised and unsupervised learning, regression, classification (k-NN, decision trees, logistic regression, SVMs), clustering, dimensionality reduction and recommenders, taught through scikit-learn labs rather than lectures alone. It sits between Harvard's statistical R-based course and the deeper academic foundations of CS50's AI.

What it does not do is go deep into the mathematical theory, and it assumes some Python, so a non-programmer should take a Python course first instead. The certificate is an optional paid extra; the material is applied and kept current.","Compare and evaluate common ML algorithms on real data"}

Machine Learning with Python: A Practical Introduction is IBM's hands-on, Python-based entry into ML. Over about five weeks it covers supervised vs unsupervised learning, regression, classification (k-NN, decision trees, logistic regression, SVMs), clustering and dimensionality reduction, and recommender systems — turning theory into skill through labs rather than lectures alone.