Compare tree ensembles: Bagging vs. Boosting. Learn the core differences between Random Forests, XGBoost, and LightGBM, how they handle bias and variance, and which algorithm to deploy for tabular data.
Kulbhushan PukaleDemystify how decision trees make splits. Explore the mathematics behind Gini Impurity, Shannon Entropy, and Information Gain, compare CART vs. ID3, and learn how to prune trees effectively.
Kulbhushan PukaleDemystify the two most foundational algorithms in ML. Learn the mathematical differences between continuous linear regression and probabilistic logistic classification, cost functions, and uses.
Kulbhushan PukaleMoving beyond the default 0.5 decision threshold. Learn how cost matrices, Youden's J statistic, Platt scaling, and Isotonic regression turn raw model scores into reliable real-world decisions.
Kulbhushan PukaleMaster regression evaluation metrics. Learn how Mean Squared Error, Root Mean Squared Error, Mean Absolute Error, and R-squared handle outliers, penalize error, and fit your ML models.
Kulbhushan PukaleUncover the critical differences between ROC-AUC and PR-AUC. Learn when False Positives distort your evaluation, how to handle severe class imbalance, and which curve fits your model best.
Kulbhushan PukaleStop relying solely on accuracy. Learn how precision, recall, and the F1 score decode the confusion matrix, handle severe class imbalance, and help you select the right evaluation metric.
Kulbhushan PukaleDemystify L1 (Lasso) and L2 (Ridge) regularization. Learn how mathematical penalties prevent overfitting, why L1 drives feature sparsity, and how to select the right technique for your models.
Kulbhushan PukaleMaster data partitioning strategies in ML. Learn how to design robust train/val/test splits, apply k-fold cross-validation, and identify subtle forms of target and train-test data leakage.
Kulbhushan Pukale
