Applied Machine Learning Algorithms
Choose, compare, tune, inspect, reject, and explain practical machine-learning algorithm families after completing Machine Learning Foundations.
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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.
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