1
Foundations
Mathematics for AI & ML
Weeks 1–4
The language every model is written in. We build just enough rigor to make everything downstream feel obvious instead of magical.
Linear Algebra
Vectors, matrices & operations
Eigenvalues & decompositions
Why it powers embeddings
Calculus & Optimization
Derivatives & gradients
Gradient descent, intuitively
Loss surfaces & convexity
Probability Basics
Distributions & expectation
Bayes' theorem in practice
Sampling & randomness
2
Reasoning with data
Statistics & Data Analysis
Weeks 5–8
Before you model anything, you learn to interrogate data honestly — the difference between a real signal and a lucky one.
Descriptive & Inferential
Central tendency & spread
Confidence intervals
Hypothesis testing
Regression & Correlation
Linear & logistic models
Correlation vs causation
Reading model coefficients
Experiment Design
A/B tests done right
p-values & significance
Avoiding common traps
3
Core craft
Machine Learning
Weeks 9–15
The heart of the program. You'll build, train, and evaluate models end-to-end — and learn why they behave the way they do.
Hands-on
Supervised Learning
Trees, forests & boosting
SVMs & k-NN
Feature engineering
Unsupervised Learning
Clustering & segmentation
Dimensionality reduction
Anomaly detection
Hands-on
Deep Learning
Neural nets from scratch
CNNs & RNNs
PyTorch & TensorFlow
Model Evaluation
Bias–variance tradeoff
Cross-validation
Precision, recall, ROC
4
The frontier
Artificial Intelligence
Weeks 16–21
Modern AI in three layers: the concepts behind large models, the tools that put them to work, and the agentic systems that chain them together.
AI Concepts
Transformers & attention
How LLMs are trained
Embeddings & vector search
Hands-on
AI Tools & Prompting
Prompt engineering that works
APIs: OpenAI, Claude, open models
RAG & fine-tuning basics
Capstone-ready
Agentic AI Workflows
Tool use & function calling
Multi-step agents & memory
Orchestration frameworks
5
Ship & get hired
MLOps & Career Launch
Weeks 22–24
Turning working models into deployed products, plus the interview prep and portfolio polish that gets you the offer.
Deployment & MLOps
Serving models as APIs
Docker & cloud basics
Monitoring in production
Portfolio Build-out
Ship your capstone live
Case-study write-ups
GitHub & demo polish
Interview Prep
ML system-design drills
Mock technical interviews
Resume & referral support
