10 questions
AI & Machine Learning: Foundations and the ML Workflow
ML workflowProblem framingSupervised vs unsupervisedBaseline modelsTraining vs inferenceWhen not to use ML
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AI & Machine Learning practice sets
Showing 1-5 of 5 unit-level practice sets
10 questions
AI & Machine Learning: Data Preparation and Features
Data qualityMissing dataFeatures and labelsEncodingScalingExploratory analysis
10 questions
AI & Machine Learning: Supervised Learning
ClassificationRegressionLinear modelsDecision treesk-nearest neighboursLoss functions
10 questions
AI & Machine Learning: Model Evaluation
Train/test splitCross-validationConfusion matrixPrecision and recallOverfittingBias and variance
10 questions
AI & Machine Learning: Unsupervised Learning and Responsible AI
ClusteringDimensionality reductionModel limitationsBias and fairnessTransparencyResponsible deployment