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