← 📊 Evaluation & ML FoundationsNEXT IN EVALUATION & ML FOUNDATIONSBenchmarks and Their Limits→
Core
Multi-Armed Bandits: Epsilon-Greedy, UCB, Thompson Sampling
How bandits trade exploration against exploitation, what epsilon-greedy, UCB, and Thompson sampling each do, and when a bandit beats a fixed A/B test.
a free account unlocks the core curriculum tier · no card
RELATED CONCEPTS
PRACTICE THIS IN REAL QUESTIONS
Machine Learning & Data ScienceWhat are multi-armed bandits, and when do you use them instead of A/B testing?→MLOps & ML EngineeringDesign an online experimentation (A/B testing) platform for ML models at scale.→LLM & GenAI FundamentalsOffline vs online evaluation: what each measures and why you need both.→Machine Learning & Data ScienceYour churn model's AUC jumps from 0.71 to 0.93 after adding a 7-day rolling feature. What now?→Machine Learning & Data ScienceDefine precision, recall, F1, and AUC, and give a case where each (and accuracy) is misleading.→Machine Learning & Data ScienceDesign an A/B test for a model change: power, sample size, significance, and the peeking problem.→
COMPANIES THAT ASSUME THIS
