Statistics Probability and conditional probability Hypothesis testing p-values and confidence intervals Bias and variance Sampling methods A/B testing Regression interpretation Machine Learning Supervised vs. unsupervised learning Linear and logistic regression Decision trees and random forests Gradient boosting (e.g., XGBoost) Cross-validation Overfitting and regularization Evaluation metrics: Accuracy Precision Recall F1-score ROC-AUC Math Basic calculus concepts (derivatives and optimization) Matrix algebra Linear algebra fundamentals Expected value and variance Bayes' theorem Optimization concepts used in machine learning