Data Science & ML

Syllabus

1 Mathematical Foundations Free preview

Vectors & GeometryMatrix AlgebraLinear Systems & Numerical MethodsDecomposition & Spectral MethodsCalculus & Optimization

2 Probability & Statistics Pro

Probability TheoryStatistical Inference

3 Core Machine Learning Pro

Data Preparation & Feature EngineeringLinear ModelsTree Models & EnsemblesInstance-Based, Kernel & Probabilistic Methods

4 Model Evaluation & Selection Pro

Core MetricsValidation & Tuning

5 Unsupervised Learning Pro

ClusteringDimensionality Reduction & Anomaly Detection

6 Deep Learning Foundations Pro

Neural Units & ActivationsBackpropagation, Training & OptimizationInitialization, Normalization & RegularizationSequence Models & Generative Models

7 Computer Vision Applications Pro

Core Vision OperationsDetection, Video & Advanced Vision

8 NLP Applications Pro

Text Representation & Classical NLPText Generation & NLP Evaluation

9 Search, Ranking & Retrieval Pro

Retrieval & Ranking Systems

10 Transformers & LLMs Pro

Attention MechanismsLLM Inference & Memory SystemsMoE, Compression & Scaling

11 Reinforcement Learning Pro

Foundations & Tabular RLAdvanced & Deep RLAdvanced RL Theory, Planning & TD LearningRL Environments, Games & Applications

12 Multimodal AI Pro

Vision-Language & Cross-Modal Systems

13 Alignment & Preference Learning Pro

RLHF, Reward Modeling & Human Feedback

14 AI Systems & MLOps Pro

Data Pipelines, Monitoring & ReliabilityInfrastructure, Parallelism & Hardware Efficiency

15 Robotics / Control Pro

Planning, Dynamics & Decision Systems

16 Frontier & Specialized Topics Pro

Representation Learning, Advanced Theory & Miscellaneous