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