Big Data & ML·Course
Big Data & Machine Learning
Big data: Hadoop, Spark, streaming, feature engineering, GNN, MLOps, and responsible AI
5
Modules
15
Articles
~2 h
Reading
IV
CLOs
§ 01 — Curriculum
5 modules.
Each module is a small unit. Most read in sequence — but a determined reader can begin anywhere.
- M IModern Machine Learning MethodsGradient boosting, ensemble methods, reinforcement learning3 articles
18 minBegin → - M IIMathematical Foundations of Deep LearningApproximation theory, backpropagation, transformers3 articles
18 minBegin → - M IIIHigh-Dimensional StatisticsCurse of dimensionality, sparsity, LASSO, Ridge, PCA3 articles
18 minBegin → - M IVConvex Optimization for MLProximal gradient methods, Adam, SGD, convergence theory3 articles
18 minBegin → - M VAlgorithms for Big DataRandomized linear algebra, hashing, streaming algorithms3 articles
18 minBegin →
§ 02 — Learning outcomes
4 outcomes.
CLO I
Data Infrastructure
Design and use big data processing systems (Hadoop, Spark, Kafka)
CLO II
Feature Engineering
Develop features for ML models and process structured and unstructured data
CLO III
Advanced Architectures
Apply graph neural networks and specialized deep learning architectures
CLO IV
MLOps and Ethics
Deploy ML systems in production and ensure fairness and explainability
§ 03 — Practices