Free Course on the Latest Intelligentsia from Agentic AI by Ernest Ng

Agentic AI's course content

• Four Intelligent Body Design Patterns
  • Reflection: Enable intelligences to check their output and think about how to improve it. For example, an AI writing assistant can automatically check grammar, logical coherence and suggest changes after generating a first draft.
  • Tool UseThe LLM-driven application can independently decide which functions to call to perform tasks, such as searching the web, accessing the calendar, sending emails, writing code, etc. The LLM-driven application can also be used as an interface to the LLM-driven application. For example, when an AI body receives the command "Book me a meeting room for next Wednesday", it can automatically check the calendar, send an email to the relevant person, and book the meeting room.
  • Planning: Use LLM to break down complex tasks into multiple executable subtasks.
  • Multi-agent Collaboration (MAC): Build multiple specialized intelligences and have them collaborate on tasks in the same way that a company hires different employees to complete complex projects.
• Evaluation and Error Analysis Process: The course emphasizes the importance of establishing a disciplined process of evaluation and error analysis to help learners efficiently locate parts of complex intelligences' processes that need improvement, and instead of intuitively guessing what to optimize next, use evaluation data to guide decisions.
• Deep Research Intelligent Body Building: In the course, learners will co-construct a Deep Research Agent that is capable of searching, synthesizing, and reporting information, integrating all of the above intelligent body design patterns and best practices.

Agentic AI's course objectives

• Mastering Intelligent Body Design Patterns
  • Reflection: Learn to have intelligences check their output and think about how to improve it.
  • Tool Use: Master how to let intelligences autonomously decide which functions to call to perform tasks such as web searches, accessing calendars, and so on.
  • Planning: Learn to utilize LLM to break down complex tasks into multiple executable subtasks.
  • Multi-agent Collaboration (MAC): Building multiple specialized intelligences to achieve collaborative task completion.
• Learning to evaluate and analyze errors: Establish a disciplined process of evaluation and error analysis to efficiently locate parts of complex intelligences' processes that need improvement, and use the evaluation data to guide decision-making.
• Building Deep Research IntelligenceThe Deep Research Agent (DRA) is a deep research agent that synthesizes all intelligent body design patterns and best practices.
• Understanding the Principles of Intelligent Body Construction: Implemented via native Python without any framework dependencies, a clear understanding of the principles of each step lays the foundation for future implementation of the same functionality on any framework.
• Developing Practical Skills: Develops learners' hands-on skills through high-quality video lectures, executable code examples, and challenging programming assignments.
• Applied Intelligent Body Technology: To help learners apply intelligent body technology to practical work to enhance productivity and innovation.

Agentic AI's program features

• Self-paced and vendor-neutral: A self-paced learning approach is used, allowing students to flexibly schedule their learning time according to their own circumstances. The course content does not rely on specific frameworks or vendors, and is implemented using native Python, giving students the freedom to implement the same functionality on any framework or without a framework, with great versatility and flexibility.
• Integration of theory and practice: Continuing the consistent teaching style of Prof. Ernest Wu, the lectures closely combine theoretical explanations with code practice. Through high-quality video lectures, the design patterns and construction principles of intelligent bodies are explained in depth; at the same time, executable code examples and challenging programming assignments are provided, so that students can consolidate what they have learned and enhance their practical operation ability in practice.
• Systematic construction of intelligencesThe course focuses on the construction of a Deep Research Agent, and systematically guides participants to complete the construction of an intelligent body through the integrated use of design modes such as reflection, tool use, planning and multi-intelligent body collaboration, as well as evaluation and error analysis processes. This systematic teaching approach helps students to deeply understand the process of constructing an intelligent body and master the complete knowledge system.
• Highly practical: The course content is closely centered on practical application scenarios, emphasizing the application of intelligent body technology in actual work. During the learning process, students will be able to understand the application cases of intelligent bodies in different fields, such as intelligent writing assistant, intelligent schedule management, etc., so as to better apply what they have learned to actual projects and improve their work efficiency and innovation ability.
• Emphasis on evaluation and error analysis: The course specifically emphasizes the importance of establishing a disciplined process for evaluation and error analysis. Evaluation data is used to guide decision making, helping participants efficiently locate parts of complex intelligences processes that need improvement, and instead of guessing intuitively what to optimize next, they can use scientific methods to optimize and improve the performance and reliability of their intelligences.

Agentic AI's course official website

• Official website address:: https://www.deeplearning.ai/courses/agentic-ai/

Who Agentic AI is for

• Artificial Intelligence DevelopersDevelopers who wish to gain an in-depth understanding of smart body technology and apply it to real projects can learn smart body design patterns and construction methods, and improve their development capabilities through the course.
• Machine Learning Engineer: Those who have a foundation in machine learning and want to further explore the application of intelligences to complex tasks, the theoretical and practical content of the course helps them to expand their technical field.
• natural language processing (NLP) researcher: Researchers focusing on the field of natural language processing, the intelligent body design and language modeling applications in the course can provide new ideas and methods for their research.
• data scientist: Data scientists who need to build intelligent systems to deal with complex data and tasks are helped to optimize the performance of their intelligences by content such as the evaluation and error analysis process in the course.
• Computer science students: Computer science majors interested in artificial intelligence and intelligent body technology, the course content helps them build a body of knowledge and a solid foundation for their future careers.
• technology enthusiast: Technology enthusiasts who are passionate about emerging technologies and want to explore the application of smart body technologies in different fields, the practical and pragmatic nature of the course will satisfy their curiosity and learning needs.