Каталог курсов
12 курсов от LLM-основ до production-grade мультиагентных паттернов. Фильтры в URL — ссылку можно сохранить или поделиться.
- Средний3126m
Multi-Agent Systems
Design, build, and operate systems where multiple AI agents collaborate to solve complex tasks that exceed the capabilities of any single agent. This advanced course covers the major multi-agent architectures — hierarchical, peer-to-peer, and market-based — and dives deep into the technical challenges: inter-agent communication protocols, shared state management, deadlock prevention, and orchestration frameworks like LangGraph and CrewAI. The final module addresses production deployment, observability for distributed agent pipelines, and failure modes unique to multi-agent systems.
- multi-agent
- langgraph
- crewai
- orchestration
- Средний3123m
Advanced Agent Patterns
Go beyond basic ReAct agents to explore sophisticated architectures used in production AI systems. This course covers Plan-and-Execute, Reflexion, LATS (Language Agent Tree Search), and custom orchestration patterns. You will learn how to rigorously evaluate agent performance using benchmark datasets and implement production hardening techniques including circuit breakers, fallback chains, observability instrumentation, and cost controls.
- advanced
- plan-execute
- reflexion
- lats
- Начальный3120m
Building Your First Agent
A hands-on project course where you build a complete, deployable AI agent from scratch. You will set up the project architecture, implement the core reasoning loop, integrate tools and memory, write tests, and deploy the agent. By the end you will have a portfolio-ready project demonstrating real agent engineering skills — not just theory.
- project
- deployment
- testing
- python
- Начальный3120m
Tools and Memory for Agents
Learn how to give AI agents superpowers through tools and memory systems. This course covers function calling, custom tool creation, and the major memory architectures — short-term conversation buffers, vector-store long-term memory, and external structured memory. By the end you will be able to build agents that remember user preferences, retrieve relevant context from large knowledge bases, and use external APIs seamlessly.
- tools
- memory
- function-calling
- vector-store
- Начальный3117m
Prompt Engineering 101
Master the craft of writing prompts that reliably produce the outputs you need. This course covers core design principles — clarity, specificity, and role framing — before moving into advanced techniques like structured output coercion, constitutional AI constraints, and prompt chaining. The final module teaches you how to systematically evaluate and improve your prompts using evals and automated testing frameworks.
- prompt-engineering
- llm
- evaluation
- beginner
- Начальный3114m
LLM Fundamentals
Build a solid understanding of how large language models work under the hood. This course demystifies transformers, tokenization, and attention mechanisms without requiring a PhD in mathematics. You will then apply that understanding to practical prompt engineering and learn to integrate LLMs via API — including OpenAI and Anthropic — with proper error handling, rate limiting, and cost management.
- llm
- transformers
- api
- openai
- Начальный3117m
Introduction to AI Agents
Discover the world of AI agents — autonomous software systems that perceive their environment, reason about goals, and take actions to accomplish tasks. This beginner-friendly course covers what agents are, how they differ from standard chatbots, the main architectural patterns used in modern AI agents, and how to build your first working agent using LangChain. By the end you will have a solid conceptual foundation and hands-on experience constructing a simple but functional agent.
- ai-agents
- langchain
- beginner
- python