AI-ML tests

28 topics, five levels each. Papers are timed, marked, and keyed — print one and sit it properly.

Study notes →

Interleaved practice — mixed topics, spaced like the real thing

Phase 1 Phase 2 Phase 3 Phase 4 Phase 5 Phase 6

TopicL1L2L3L4L5

AI Agents & Tool UseL1 L2 L3 L4 L5
AI Safety & AlignmentL1 L2 L3 L4 L5
Alignment, Prompting & RAGL1 L2 L3 L4 L5
Calculus & Optimization BasicsL1 L2 L3 L4 L5
Convolutional Neural NetworksL1 L2 L3 L4 L5
Data Preprocessing & Feature EngineeringL1 L2 L3 L4 L5
Deep & Advanced RLL1 L2 L3 L4 L5
Deep Learning FrameworksL1 L2 L3 L4 L5
Generative ModelsL1 L2 L3 L4 L5
Interpretability & ExplainabilityL1 L2 L3 L4 L5
Linear & Logistic RegressionL1 L2 L3 L4 L5
Linear Algebra EssentialsL1 L2 L3 L4 L5
MLOps & DeploymentL1 L2 L3 L4 L5
Model Evaluation & SelectionL1 L2 L3 L4 L5
Neural Network FundamentalsL1 L2 L3 L4 L5
Pretraining & Fine-Tuning LLMsL1 L2 L3 L4 L5
Probability & StatisticsL1 L2 L3 L4 L5
Python & Scientific ComputingL1 L2 L3 L4 L5
Reinforcement Learning FoundationsL1 L2 L3 L4 L5
Research Frontiers & PracticeL1 L2 L3 L4 L5
Scaling & Efficient ArchitecturesL1 L2 L3 L4 L5
Sequence ModelsL1 L2 L3 L4 L5
SVM, Naive Bayes & Probabilistic ModelsL1 L2 L3 L4 L5
Tokenization & Language ModelingL1 L2 L3 L4 L5
Training Deep NetworksL1 L2 L3 L4 L5
Transformer ArchitectureL1 L2 L3 L4 L5
Tree-Based & Instance MethodsL1 L2 L3 L4 L5
Unsupervised LearningL1 L2 L3 L4 L5