Applied Machine Learning Algorithms
Choose, compare, tune, inspect, reject, and explain practical machine-learning algorithm families after completing Machine Learning Foundations.
Account-free and browser-localProgress is stored only in this browser. Browser notebooks are available through the Applied ML lab, and the complete download pack remains available for local fallback. The course is educational material, not grading, certification, or production approval.
Progress overview
Course sequence
- Module 1: Algorithm Selection as a Design ChoiceThis module teaches learners that choosing an algorithm is a design decision, not a race through a menu of model names.
- Module 2: Regularised Linear and Logistic ModelsThis module helps learners deepen their use of linear models beyond the foundation baseline.
- Module 3: Distance-Based Methods and Kernel IntuitionThis module helps learners understand when distance is meaningful.
- Module 4: Probabilistic Classifiers and CalibrationThis module helps learners use algorithms and checks where probabilities matter.
- Module 5: Support Vector MachinesThis module helps learners use SVMs as practical tools with clear boundaries.
- Module 6: Trees, Pruning, and Rule-Like ModelsThis module helps learners move from simple tree use to honest tree control.
- Module 7: Ensembles: Bagging, Forests, Boosting, Voting, and StackingThis module helps learners understand why ensembles often perform well and what they cost.
- Module 8: Feature Selection, Dimensionality, and InspectionThis module helps learners reduce and inspect models responsibly.
- Module 9: Special Problem SettingsThis module helps learners recognize important model settings without turning them into full specialist courses.
- Module 10: Capstone: Algorithm Portfolio and Selection MemoThis module helps learners produce an honest algorithm portfolio instead of a single leaderboard.
Local learning achievements
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