| name | self-project |
| description | Python machine learning with scikit-learn, PyTorch, and TensorFlow |
| version | 2.1.0 |
| sasmp_version | 1.3.0 |
| bonded_agent | 03-data-science |
| bond_type | PRIMARY_BOND |
| retry_strategy | exponential_backoff |
| observability | {"logging":true,"metrics":"model_accuracy"} |
Self Project 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
