Autonomous AI agents are getting more popular day by day; however, most professionals do not know what they are, how they work, or how to build one. If you want to know more about these AI agents and build one that can talk, reason, and act on your behalf, now is the best time to learn about these autonomous AI systems. In this article, there are 9 popular courses to start learning about AI agents. We divided this article into 3 sections: Beginners, Intermediate, and Advanced courses.
These 9 courses listed in this article will help you get started with AI agents and build practical, scalable AI agents in no time. There's something here for everyone to help you level up your skills, whether you're a complete beginner or have technical skills and knowledge.
Here are the 9 popular courses to start learning about AI agents:
Beginner Courses
1. Deep Learning.AI: AI Python for Beginners
This four-part course is designed for those who want to learn Python for practical AI applications, regardless of their programming experience. It takes a hands-on approach, guiding you through the process of building AI-powered tools from day one. With the help of an AI chatbot, you'll get immediate feedback and support as you learn.
- Practical Projects: Build custom recipe generators, smart to-do lists, and vacation planners.
- Foundational Skills: Learn essential programming concepts like variables, functions, loops, and data structures.
- Data Analysis: Work with your own data, extracting insights from text files and structured data.
- API Integration: Learn to access real-time information through APIs and use popular third-party packages for data analysis.
2. Vanderbilt University: Agentic AI and AI Agents for Leaders Specialization
This specialization course is for leaders who want to understand and implement Agentic AI in their organizations. It focuses on strategic decision-making and driving innovation through AI agents. You'll gain the skills to design, evaluate, and deploy AI tools that have a real-world impact.
- Leadership Focus: Understand how to drive innovation and strategic decision-making with AI.
- Custom GPTs: Design basic AI agents using custom GPTs to tackle authentic challenges.
- Human-in-the-Loop: Build agents that blend human oversight with AI efficiency.
- Real-World Impact: Learn to harness automation for practical applications in your organization.
3. LangChain: Basics of LangGraph
This course will introduce you to LangGraph, a framework for building agentic and multi-agent applications. It's separate from the main LangChain package and is designed to give developers more precision and control over their agentic workflows.
- LangGraph Framework: Learn the basics of this powerful framework for building agentic applications.
- Multi-Agent Applications: Understand how to create and manage applications with multiple agents.
- Precision and Control: Add a higher degree of control to your agentic workflows.
- Practical Introduction: Get a straightforward introduction to a key tool in the AI agent ecosystem.
Intermediate Courses
4. Langchain and Tavily: AI Agents in LangGraph
In this course, you'll learn to build a highly controllable AI agent from scratch using Python and a large language model (LLM). You'll then rebuild it using LangGraph, gaining a deep understanding of its components and how to create flow-based applications.
- Build from Scratch: Create an agent from the ground up to understand the division of tasks between the LLM and the code.
- Agentic Search: Learn about agentic search that returns multiple answers in an agent-friendly format.
- Persistence: Implement state management in your agents, allowing for conversation switching and reloading previous states.
- Human-in-the-Loop: Incorporate human oversight into your agent systems for better control.
5. Anthropic: MCP: Build Rich-Context AI Apps with Anthropic
DeepLearning.AI, in partnership with Anthropic, created this course that introduces you to Model Context Protocol (MCP), an open protocol that standardizes how LLMs access external tools and data. You'll learn how to build rich-context AI applications with minimal integration work.
- Model Context Protocol (MCP): Understand how MCP simplifies connecting AI applications to external data sources.
- Client-Server Architecture: Learn the core components of MCP's client-server architecture.
- Build and Deploy: Create a chatbot, make it MCP-compatible, and deploy an MCP server.
- Ecosystem Integration: Connect your chatbot to a growing ecosystem of MCP servers.
6. Llamaindex: Building Agentic RAG with Llamaindex
Taught by the co-founder and CEO of LlamaIndex, this course teaches you how to build research agents skilled in tool use, reasoning, and decision-making with your data. You'll learn to build an autonomous research agent that can intelligently understand and analyze information.
- Agentic RAG: Build a router that can pick the right query engine for a given task.
- Tool Calling: Add tool calling to your agent, allowing the LLM to infer which function to execute.
- Multi-Document Agent: Extend your research agent to handle multiple documents at once.
- Debugging: Learn how to debug your agents to guide their actions effectively.
Advanced Courses
7. Arize: Evaluating AI Agents
Another course by DeepLearning.AI, but this time made in partnership with Arize AI, which can teach you how to systematically assess and improve your AI agent's performance. You'll learn how to structure your evaluations to identify areas for improvement and refine your agent's performance.
- Systematic Evaluation: Learn to assess the performance of each component of your agent.
- Observability: Add observability to your agent to visualize and debug its steps.
- Evaluator Selection: Choose the right evaluators, testing examples, and metrics for each component.
- Structured Experiments: Run experiments to improve your agent's performance by exploring changes to the prompt, model, or logic.
8. AGI Inc: Building AI Browser Agents
Taught by the co-founders of AGI Inc, this course shows you how to build AI agents that can interact with websites. You'll learn how these agents use visual and structural data to reason and take actions, and how to make them more robust and reliable.
- Web Automation: Build an agent that can scrape websites and return structured data.
- Autonomous Agents: Create an agent that can execute multiple tasks, like filling out forms and signing up for newsletters.
- AgentQ Framework: Explore a framework that allows agents to self-correct through a combination of advanced algorithms.
- Future of Web Agents: Understand the key factors shaping the evolution of AI agents, including hardware, algorithms, and data availability.
9. Guardrails AI: Self and reliable AI via Guardrails
This course, taught by the co-founder and CEO of GuardrailsAI, shows you how to build safe and reliable AI applications. You'll learn to create guardrails that mitigate common failure modes of LLMs, such as hallucinations and revealing sensitive information.
- Mitigate Failure Modes: Learn to prevent common issues like hallucinations and going off-topic.
- Input and Output Guards: Create guardrails to validate and verify your application's responses.
- PII Protection: Add a guardrail to detect and redact personally identifiable information.
- Production-Ready Applications: Build robust applications ready for real-world use in regulated industries.
In Conclusion:
Autonomous AI agents are getting more popular day by day, as we speak, and if you want to learn more about AI agents and build one yourself, the courses mentioned above are amazing resources and a great start for both technical and non-technical professionals. If you invest your time wisely in learning these industry-standard resources, you'll be well-equipped with the knowledge and skills to build an intelligent, autonomous AI agent. Don't let this opportunity pass you by; start learning and building today.
