Strengthen your data science foundation for AI. Master statistics, feature engineering, model selection, and production deployment.
Review probability distributions, hypothesis testing, Bayesian inference, and confidence intervals.
Handle missing values, outliers, categorical encoding, and feature scaling. Build robust data pipelines.
Create powerful features: polynomial, interaction, time-based, and domain-specific transformations.
Choose the right algorithm for your problem. Compare linear models, tree-based methods, and neural networks.
Combine models for better performance: bagging, boosting, stacking, and blending techniques.
Explain model predictions with SHAP, LIME, and feature importance. Build trust with stakeholders.
Package, version, and deploy models. Build APIs, batch inference, and real-time serving.
Deploy models to cloud platforms with automated scaling, monitoring, and CI/CD integration.