AI & Machine Learning
Level 11 — AI, ML, deep learning, LLMs, RAG and agents explained from zero, with hands-on projects.
- AI vs ML vs Deep LearningFoundation
Understand the landscape from scratch — what AI, ML, and deep learning actually are, how they differ, supervised/unsupervised/reinforcement learning, and how every major company uses them.
aimachine-learningdeep-learningsupervisedunsupervisedreinforcement-learning - Math Foundations for MLFoundations
The minimal math that makes ML stop being magic: vectors as data, dot products as similarity, gradients as direction, and probability as honesty.
mathlinear-algebraprobabilitycalculusml - Classical MLFoundations
Regression, trees and clustering — the algorithms that still win on tabular data, plus the evaluation discipline (precision, recall, overfitting) that carries to all of ML.
machine-learningregressionclassificationclustering - Neural NetworksIntermediate
From single perceptrons to deep networks — how neural networks learn, backpropagation, activation functions, CNNs for images, and building your first network in Python.
neural-networksdeep-learningbackpropagationcnnpytorchtensorflow - Transformers & AttentionAdvanced
Why attention replaced RNNs, how self-attention works (Q/K/V, scaled dot-product, softmax), multi-head attention, positional encoding, the transformer block, and the O(n²) cost behind context-window limits.
transformersattentionself-attentionnlpdeep-learningpositional-encoding - Large Language Models (LLMs)Intermediate
How LLMs work — from tokens to transformers to in-context learning. What prompting, temperature, and context windows mean, and how to build production applications with them.
llmgpttransformerspromptingainlp - RAG — Retrieval-Augmented GenerationIntermediate
Give LLMs access to your own data — how RAG works, vector embeddings, chunking strategies, re-ranking, and building production RAG pipelines.
ragllmembeddingsvector-databaseretrievalai - AI AgentsAdvanced
Autonomous AI systems that plan, use tools, and execute multi-step tasks — how agents work, the ReAct pattern, tool use, memory, and building your first agent.
agentsllmtool-usereactautonomous-aiai - Vector DatabasesIntermediate
How vector databases store and search embeddings — HNSW, IVF, product quantization, and a comparison of Pinecone, Weaviate, ChromaDB, and pgvector.
vector-databaseembeddingshnswannpineconechromadbpgvector - Fine-TuningIntermediate
Teaching a pre-trained model new behavior — full fine-tuning vs LoRA, when to fine-tune vs RAG vs prompting, data preparation, and evaluation.
fine-tuninglorallmtrainingai - LLM Evaluation & SafetyAdvanced
How to evaluate non-deterministic LLM outputs (golden sets, LLM-as-judge, offline vs online), reduce hallucination, and defend against prompt injection and unsafe output with input/output guardrails.
llmevaluationsafetyguardrailsprompt-injectionhallucination - MLOps & Model DeploymentAdvanced
The half of ML that isn't modeling: serving (batch vs online), the deploy pipeline and model registry, shipping safely (shadow/canary/rollback), monitoring data & concept drift, training-serving skew, and retraining triggers.
mlopsdeploymentmonitoringdriftservingmodel-registry - NumPy & Pandas for Machine LearningFoundation
The two libraries every ML engineer reaches for first — NumPy for fast numerical arrays, Pandas for structured data manipulation. Learn them from zero, use them confidently in every ML pipeline.
numpypandasdata-manipulationmachine-learningpythonarraysdataframes