Machine Learning Engineer Machine Learning EngineerMathematics and StatisticsLinear AlgebraCalculusProbability and StatisticsOptimization TechniquesProgramming LanguagesPythonRJavaC++MATLABMachine Learning AlgorithmsSupervised LearningRegressionClassificationDecision TreesRandom ForestsSupport Vector Machines (SVM)Unsupervised LearningClusteringDimensionality ReductionAssociation RulesAutoencodersGenerative Adversarial Networks (GANs)Reinforcement LearningMarkov Decision Processes (MDP)Q-LearningDeep Q-Networks (DQN)Policy Gradient MethodsActor-Critic ModelsDeep LearningNeural NetworksConvolutional Neural Networks (CNN)Recurrent Neural Networks (RNN)Long Short-Term Memory (LSTM)Generative Models (e.g., GANs, VAEs)Machine Learning Frameworks and LibrariesTensorFlowKerasPyTorchscikit-learnApache Spark MLlibData Preprocessing and Feature EngineeringData CleaningData TransformationFeature SelectionFeature ScalingHandling Missing DataModel Evaluation and ValidationPerformance Metrics (e.g., Accuracy, Precision, Recall, F1 Score)Cross-ValidationHyperparameter TuningModel SelectionOverfitting and UnderfittingBig Data and Distributed ComputingApache HadoopApache SparkDistributed File Systems (e.g., HDFS)Data ParallelismModel ParallelismSoftware Engineering and Version ControlObject-Oriented Programming (OOP)Design PatternsUnit TestingCode DocumentationGit and Version Control SystemsDeployment and ProductionizationModel DeploymentContainerization (e.g., Docker)Cloud Platforms (e.g., AWS, Azure, GCP)Model Monitoring and MaintenanceScalability and Performance OptimizationData Visualization and CommunicationData Visualization Libraries (e.g., Matplotlib, ggplot, D3.js)Interactive Dashboards (e.g., Tableau, Power BI)Storytelling with DataEffective Communication of ResultsReporting and Presentation Skills