Classic Supervised Learning Algorithms

61 lessons in AI Fundamentals

• Interpretability of Classic ModelsSlides / Video
• Algorithm Selection and Trade-offsSlides / Video
• Multi-Class Classification StrategiesSlides / Video
• Linear vs Non-Linear Decision BoundariesSlides / Video
• Parametric vs Non-Parametric AlgorithmsSlides / Video
• L1 and L2 Regularization BasicsSlides / Video
• The Kernel Trick ExplainedSlides / Video
• Support Vector Machines and MarginsSlides / Video
• Naive Bayes Independence AssumptionSlides / Video
• Naive Bayes ClassifierSlides / Video
• K-Nearest Neighbors AlgorithmSlides / Video
• Sigmoid and Decision BoundariesSlides / Video
• Logistic Regression for ClassificationSlides / Video
• Cost Function and Least SquaresSlides / Video
• Linear Regression: Model and AssumptionsSlides / Video
• Polynomial RegressionQuiz
• Assumptions of Linear RegressionQuiz
• Linear Regression FundamentalsQuiz
• Random Forests and BaggingQuiz
• Parametric vs Non-ParametricQuiz
• Decision Tree PruningQuiz
• OLS and Cost FunctionQuiz
• Linear vs Non-Linear ModelsQuiz
• Ensemble Methods OverviewQuiz
• Naive Bayes Independence AssumptionQuiz
• Elastic Net RegularizationQuiz
• Ridge Regularization (L2)Quiz
• K-Nearest Neighbors AlgorithmQuiz
• Decision Boundaries in ClassificationQuiz
• Multiclass StrategiesQuiz
• Logistic Regression and SigmoidQuiz
• Choosing K and Distance MetricsQuiz
• SVM and MarginsQuiz
• Decision Tree SplittingQuiz
• Lasso (L1) and Feature SelectionQuiz
• Naive Bayes ClassifierQuiz
• Gini and Entropy ImpurityQuiz
• The Kernel TrickQuiz
• Soft Margin and C ParameterQuiz
• Algorithm Selection Trade-offsQuiz
• Boosting ConceptQuiz
• Feature Importance from TreesQuiz
• Polynomial RegressionFlashcard
• Linear vs Non-Linear ModelsFlashcard
• Multiclass Strategies: OvR vs OvOFlashcard
• Assumptions of Linear RegressionFlashcard
• Choosing an Algorithm for a ProblemFlashcard
• Parametric vs Non-Parametric ModelsFlashcard
• Lazy vs Eager LearningFlashcard
• Sigmoid and Log-OddsFlashcard
• Decision BoundaryFlashcard
• Margin and Support VectorsFlashcard
• Linear Regression BasicsFlashcard
• Choosing K in KNNFlashcard
• Naive Bayes ClassifierFlashcard
• K-Nearest NeighborsFlashcard
• Support Vector MachinesFlashcard
• Bayes Theorem and IndependenceFlashcard
• Logistic Regression BasicsFlashcard
• The Kernel TrickFlashcard
• Regularization: L1 and L2Flashcard