Machine Learning A-Z: AI, Python & R

Machine Learning A-Z (Kirill Eremenko & Hadelin de Ponteves of SuperDataScience/Ligency) is Udemy's most-reviewed ML course: 4.5 stars across roughly 203,000 ratings and over 1,087,000 students. Across ~42.5 hours of video plus 40 articles, it sprints through the full classical-ML map in both Python and R: data preprocessing, regression, classification, clustering, association rule learning, reinforcement learning, NLP, basic deep learning (ANN/CNN), dimensionality reduction, and model selection/boosting (XGBoost). Its real strength is breadth-for-beginners and exceptional value (list ~$119.99 but almost always on sale near $12.99). Its real weakness, echoed consistently on Reddit and in editorial rankings, is depth: it is intuition-light and template-driven (you download and run code rather than derive it), the math is high-school level, the R track is widely skipped, and graduates report understanding terminology without being job- or production-ready. Best as a confidence-building first tour, not a rigorous or career-finishing course.

An excellent, high-value first overview for absolute beginners who want broad exposure to many ML algorithms with runnable code, but conditional because it is deliberately shallow: light on math derivation, template/black-box driven, weak assessments, and not sufficient alone for ML-engineering jobs or deep-learning depth. Take it for breadth and intuition; pair it with a rigorous course and real projects for mastery.

Best for: Absolute beginners and non-CS professionals who want a friendly, hands-on tour of the whole classical-ML landscape, prefer learning by running working code, value breadth over rigor, and want lifetime access at a low sale price. Also useful for analysts who want to recognize and apply many algorithms quickly, and the rare learner who specifically wants both Python and R.

Skip if: Anyone seeking mathematical rigor or the ability to implement algorithms from scratch; people targeting ML-engineer / data-scientist roles who need production-grade, job-ready skills; those wanting deep modern deep learning (the DL section is introductory); experienced programmers who will find the pace slow and the templates hand-holding; and learners who want strong graded assessments or a capstone.