AI University

🗄

AI Data

Understand datasets, preprocessing, feature engineering, embeddings, and data pipelines that power modern AI systems.

5 lessons

⚙

AI Training

Learn how AI models are trained, from supervised learning to fine-tuning and reinforcement learning.

3 lessons

💬

Prompts & Prompt Engineering

Master the art of crafting prompts for LLMs — zero-shot, few-shot, chain-of-thought, system prompts, and more.

5 lessons

▶

Inference

How trained models make predictions — model serving, quantization, latency optimization, and batching strategies.

3 lessons

☁

AI Hosting & Deployment

Deploy AI models to production — containers, serverless, edge computing, monitoring, and cost optimization.

3 lessons

🔍

RAG — Retrieval-Augmented Generation

Build systems that combine retrieval with LLMs — chunking, embedding, vector search, and hybrid retrieval strategies.

4 lessons

🤖

AI Agents

Build autonomous AI agents — tool use, planning, memory, multi-agent systems, and agentic workflows.

5 lessons

🧠

Deep Learning Basics

Foundations of deep learning — neural networks, backpropagation, CNNs, RNNs, transformers, and attention.

3 lessons