Learning Catalogue, Analytics

7 Steps to Actionable Insights

Learning Catalogue, Analytics

Discovering Hidden Patterns In Data

Learning Catalogue

Regularization and Tuning of Linear Models

Covers techniques like Ridge, Lasso, and Elastic Net to prevent overfitting and improve generalization in linear models. Also addresses hyperparameter tuning strategies to squeeze out optimal model performance.

Learning Catalogue

Online Experimentation And A/B Testing

Covers the statistical foundations and practical design of controlled experiments to measure product or feature impact. Includes topics like sample size calculation, statistical significance, and common pitfalls that invalidate results.

Learning Catalogue

Boosting

Dives into sequential ensemble techniques like AdaBoost, Gradient Boosting, and XGBoost that iteratively correct prior errors. Known for producing some of the highest-performing models in structured/tabular data competitions.

Learning Catalogue

Ensemble Learning, Bagging & Random Forest

Explores how combining multiple weak learners improves predictive accuracy and stability, with Random Forest as the flagship bagging algorithm. Highlights how bagging reduces variance compared to single decision trees.