Python Machine Learning Skill Overview Build machine learning models using Python libraries including scikit-learn, PyTorch, and supporting tools. Topics Covered Scikit-learn Data preprocessing Model selection Training pipelines Cross-validation Hyperparameter tuning PyTorch Basics Tensor operations Neural network modules Training loops DataLoader usage GPU acceleration Feature Engineering Feature selection Dimensionality reduction Feature scaling Encoding techniques Missing data handling Model Evaluation Metrics selection Confusion matrix ROC curves Learning curves Model comparison MLOps Basics Model serialization Experiment tracking (MLflow) Model versioning Serving models Reproducibility Prerequisites Python fundamentals NumPy and Pandas Statistics basics Learning Outcomes Train ML models Evaluate model performance Build ML pipelines Deploy models to production

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