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Curriculum Shelf

AI Learning Resources

Textbooks, lecture recordings, papers, and lab notebooks supporting all 6 modules.

MODULE 01 · Math Foundations3 Lessons

Module 1 — Math Foundations for ML

Linear algebra, calculus, and probability & information theory essential for AI and machine learning interviews.

1.1 Linear Algebra1.2 Calculus for ML1.3 Probability & Information Theory
MODULE 02 · Core ML Theory5 Lessons

Module 2 — Core ML Theory

Supervised learning, regularization, optimization algorithms, classical ML models, and evaluation metrics.

2.1 Supervised Learning Basics2.2 Regularization2.3 Optimization2.4 Classical ML Algorithms
MODULE 03 · Deep Learning Theory5 Lessons

Module 3 — Deep Learning Theory

Neural network basics, CNNs, sequence models, transformers, and representation embeddings.

3.1 Neural Network Basics3.2 CNNs (conceptual)3.3 RNNs / Sequence Models (conceptual)3.4 Transformers (highest-yield topic right now)
MODULE 04 · Modern LLMs5 Lessons

Module 4 — Modern LLM Concepts

Pretraining, fine-tuning, PEFT (LoRA/QLoRA), alignment (RLHF/PPO/GRPO/DPO), decoding, and quantization.

4.1 Pretraining vs Fine-tuning4.2 Parameter-Efficient Fine-Tuning (PEFT)4.3 RLHF & Alignment (PPO, GRPO, DPO)4.4 Inference-time Concepts
MODULE 05 · Reinforcement Learning2 Lessons

Module 5 — Reinforcement Learning

Core RL formulations, MDPs, value functions, Q-learning, DQN, policy gradients, actor-critic, and PPO.

5.1 Core RL Concepts5.2 Algorithm Families
MODULE 06 · System-Adjacent AI2 Lessons

Module 6 — System-Adjacent AI

Production inference, model versioning, A/B testing, drift detection, feature stores, and bias/fairness.

6.1 ML in Production6.2 Bias, Fairness, Practical Pitfalls