Assessment

10 Python questions to check readiness after Phase 0. Includes writing functions, finding bugs, and predicting output.

10 questions to check readiness after the Math for RL track. Solutions included.

5–8 questions to check readiness after prerequisites. Solutions included.

5 questions after completing the first 7 ML Foundations pages. Check your understanding before continuing.

12 questions covering supervised learning, gradient descent, model evaluation, and sklearn. Pass: 9/12.

5 questions after completing the first 6 DL Foundations pages. Check your understanding before continuing.

12 questions covering neural networks, backpropagation, training loops, and CNNs. Pass: 9/12.

5 quick questions after Chapters 1–5 of Volume 1. Check you're ready to continue.

5 quick questions after Chapters 11–15 of Volume 2. Check you're ready to continue.

10–15 questions on MDPs, Bellman, MC vs TD, SARSA vs Q-learning. Solutions included.

5 quick questions after Chapters 21–25 of Volume 3. Check you're ready to continue.

5 quick questions after Chapters 31–35 of Volume 4. Check you're ready to continue.

10–12 questions on DQN, policy gradient, PPO, replay, target network. Solutions included.

5 quick questions after Chapters 41–45 of Volume 5. Check you're ready to continue.

15 questions spanning Volumes 6–10: model-based RL, exploration, offline RL, multi-agent, and real-world applications.