Master Machine Learning

A production-grade curriculum for engineers who want depth, not shortcuts.

Most ML courses teach you to call model.fit(). This curriculum teaches you what happens inside - and how to build systems that work in production.

The Curriculum

Three levels. Sequential. Each builds on the last.

ML Foundations - Beginner

Who it's for: Engineers and data scientists building rigorous ML foundations from first principles.

Module	Topics
01 - ML Foundations	Bias-variance tradeoff, evaluation, generalization, probabilistic view, statistical learning theory
02 - Linear Models	Linear regression, gradient descent, logistic regression, regularization, MLE, GLMs
03 - Tree Models & Ensembles	Decision trees, random forests, gradient boosting, XGBoost, feature importance
04 - Neural Networks	Perceptron, backpropagation, activations, weight initialization, batch norm, dropout, optimizers
05 - Computer Vision	CNNs, pooling, architectures (ResNet, EfficientNet), transfer learning, detection, segmentation

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Applied ML - Intermediate

Who it's for: ML engineers who know the basics and want to build real, deployable systems.

Module	Topics
06 - Sequences & Time Series	RNNs, LSTMs, GRUs, seq2seq, attention, time series forecasting
07 - Unsupervised Learning	K-Means, hierarchical clustering, DBSCAN, PCA, t-SNE, autoencoders
08 - Recommender Systems	Collaborative filtering, matrix factorization, content-based, neural recsys
09 - ML Engineering with Python	NumPy, Pandas, scikit-learn pipelines, PyTorch, Hugging Face
10 - ML System Design	Problem framing, data pipelines, model serving, monitoring, case studies

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Advanced ML - Advanced

Who it's for: Senior ML engineers tackling cutting-edge research and production-scale challenges.

Module	Topics
11 - Reinforcement Learning	MDPs, dynamic programming, Q-learning, DQN, policy gradients, RLHF
12 - Explainability & Interpretability	SHAP, LIME, attention maps, mechanistic interpretability, audit frameworks
13 - Graph Neural Networks	GCN, GAT, GraphSAGE, message passing, graph classification, link prediction
14 - Bayesian Machine Learning	Bayesian inference, Gaussian processes, variational inference, uncertainty quantification
15 - Diffusion Models	Score matching, DDPM, DDIM, latent diffusion, classifier-free guidance

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What You Will Be Able to Do

After completing this curriculum:

• Explain any ML algorithm from first principles - not just name-drop it in interviews
• Debug model failures by understanding what's actually happening inside the training loop
• Design end-to-end ML systems - data ingestion, training, serving, and monitoring
• Choose the right model for a problem based on data characteristics, not habit
• Read ML research papers and implement the key ideas from scratch
• Quantify and manage uncertainty in model predictions for production reliability

The Engineering Standard

Every lesson in this curriculum:

• Opens with a real interview scenario or production failure you will encounter
• Derives the math from first principles - not handed to you as magic formulas
• Connects theory to code with working, idiomatic Python implementations
• Closes with senior-level interview Q&As that test conceptual depth

This is not a tutorial platform. It is an engineering curriculum.

Career Outcomes

Prepared for roles including:

• Machine Learning Engineer
• AI Engineer
• Data Scientist (Research & Applied)
• MLOps Engineer
• Research Engineer
• AI Systems Architect

Certification (Coming Soon)

EngineersOfAI - Machine Learning Engineering Certification

Practical. Deep. Interview-ready. For engineers who want to demonstrate real ML depth - not just familiarity with sklearn.