Very High DemandAdvanced
Machine Learning Engineer
Master Machine Learning algorithms, feature engineering, and model deployment with Python, scikit-learn, and modern ML frameworks.
8-12 months
7 Steps
$130k - $250k
Key Technologies
PythonScikit-learnMachine LearningFeature EngineeringModel DeploymentMLOpsStatistics
Your Progress0 / 7 completed
Learning Path
1.
Python & Statistics for ML
Master Python and essential statistics to understand and implement ML algorithms.
PythonNumPyPandasMatplotlibSeabornStatisticsProbabilityHypothesis Testing
4-5 weeks
2.
Supervised Learning
Master classification and regression algorithms with scikit-learn.
Linear RegressionLogistic RegressionDecision TreesRandom ForestSVMNaive Bayesk-NNCross-validation
5-6 weeks
3.
Unsupervised Learning
Explore clustering, dimensionality reduction, and anomaly detection.
K-MeansHierarchical ClusteringDBSCANPCAt-SNEAnomaly DetectionAssociation RulesDimensionality Reduction
4-5 weeks
4.
Feature Engineering & Selection
Master the art of creating and selecting features to optimize your models.
Feature CreationFeature SelectionFeature ScalingEncoding TechniquesHandling Missing DataFeature ImportanceAutomated Feature Engineering
3-4 weeks
5.
Advanced ML Algorithms
Explore advanced algorithms: ensemble methods, gradient boosting, and time series.
Ensemble MethodsXGBoostLightGBMCatBoostTime Series ForecastingBayesian MethodsMeta-LearningAutoML
6-7 weeks
6.
Model Evaluation & Optimization
Learn advanced metrics, validation, and hyperparameter optimization techniques.
Model EvaluationCross-validationHyperparameter TuningBias-Variance TradeoffModel InterpretationSHAPLIMEBayesian Optimization
4-5 weeks
7.
MLOps & Production
Deploy and maintain ML models in production with monitoring and CI/CD.
Model DeploymentREST APIsDockerModel ServingA/B TestingModel MonitoringData Drift DetectionCI/CD for ML
5-6 weeks