AI / Machine Learning

Learning modules

1. 1Python scientific stack: NumPy vectorization & Pandas data pipelines
2. 2Mathematical foundations: Linear algebra, calculus intuition & statistics
3. 3Machine learning workflow: Problem framing, data splitting & leakage prevention
4. 4Supervised regression: Linear, Ridge, Lasso & evaluation metrics (MAE, RMSE, R²)
5. 5Supervised classification: Logistic regression, SVMs, Decision Trees & Random Forests
6. 6Classification metrics: Precision, Recall, F1-score, ROC-AUC & confusion matrix
7. 7Unsupervised learning: K-Means clustering & PCA dimensionality reduction
8. 8Model tuning: Grid search, randomized search & cross-validation
9. 9Model persistence (Joblib) & building inference REST endpoints
10. 10Modern AI API integration concepts & deep learning foundational orientation
11. 11Capstone: Production-ready machine learning pipeline with live inference demonstration

Outcomes

• Understand the algorithmic mechanics and math behind core machine learning models
• Train, tune, and evaluate robust classification and regression pipelines with scikit-learn
• Prevent data leakage and overfitting using rigorous cross-validation workflows
• Save and serve trained models via clean Python API endpoints
• Integrate external AI APIs and understand neural network foundational concepts