Data Science & Developer Roadmaps with Free Learning Resources

Machine Learning Roadmap

Below you’ll find the Machine Learning roadmap - a step-by-step guide on how to become a Machine Learning Engineer. This roadmap covers topics like Machine Learning models and algorithms, statistics and important tools and frameworks.

Jump on this track after you have completed the Fundamentals roadmap and the Data Science roadmap.

All boxes are clickable and provide you with AI-powered explanations and free learning resources. You can also chat with our 🤖 bot when you have any question about the topics on this roadmap.

MethodsRegressionClassificationAssociation Rule LearningDimensionality ReductionUse-casesML with PythonDeep LearningRoadmapConcepts, Inputs & AttributesGradient DescentOverfitting & UnderfittingTraining, Validation and Test dataPrecision vs RecallBias & VarianceLiftCategorical VariablesOrdinal variablesNumerical VariablesClusteringHirarchical ClusteringK-means ClusteringDBSCANHDBSCANFuzzy C-MeansMean ShiftAgglomeratesOpticsApriori Algorithm ECLAT AlgorithmFP TreesPrincipal Component AnalysisRandom ProjectionNMFT-SNEUMAPSupervised LearningUnsupervised LearningEnsemble LearningReinforcement LearningLinear RegressionPossion RegressionClassification RateDecision TreesLogistic RegressionNaive Bayes ClassifiersK-Nearest NeighbourSupport Vector MachineGaussian Mixture ModelsQ-LearningBaggingStackingBoostingSentiment AnalysisCollaborative FilteringTaggingPredictionFrameworksFlaskDjangoKerasBottleCherrypyToolsSikit-LearnTensorFlowSpacyPandasNumpyPyTorchMatplotlibImportant LibrariesModel DeploymentDockersKubernetesGradioMLflowMachine LearningEngineer