Hands on - Building Intelligent Agents with LLMs

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Priyanka Dwivedi

6:17:38

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1. Foundation of LLM Agents

1. Introduction to the Course.mp4

05:54

2. Introduction to Unit 1.mp4

01:16

3. Understanding LLMs.mp4

10:03

4. Comparison of LLMs across benchmarks.mp4

02:54

5. LLM vs RAG vs Chain vs Agents.mp4

07:23

6. Difference between LLM and LLM Agents.mp4

03:09

7. Components of an LLM Agent.mp4

09:26

8. GPT Researcher demo.mp4

10:57

9. Setting up your API Keys.mp4

02:56

10. Access code and setup your environment.mp4

06:26

11. Coding your first Agent - Self Ask with Search.mp4

12:01

12. Coding your first agent in Colab.mp4

01:45

Files.zip

2. Unit 2 - Integrating Tools with LLMs

1. Introduction to Tools.mp4

02:44

2. Why LLMs need tools.mp4

04:06

3. Accessing and Running tools in Langchain.mp4

05:59

4. Creating custom tools in Langchain.mp4

07:40

5. ReACT agent with tools.mp4

14:07

6. Issues with too many tools.mp4

05:40

7. Recent approaches for improving tool selection.mp4

08:06

8. Tool Selection on RestBench dataset using OpenAI models.mp4

13:26

9. Tool Selection on Rest Bench dataset using Anthropic models.mp4

06:35

10. OpenAI Function Calling for tool selection.mp4

12:30

Files.zip

3. Unit 3 - Adding Memory to your LLM Agent

1. Introduction to Memory.mp4

01:50

2. Why do LLMs need a memory module.mp4

03:33

3. Different Types of Memory.mp4

03:32

4. Understanding and Coding different short term memory modules.mp4

14:24

5. Long term memory and RAG.mp4

03:01

6. Coding a basic RAG pipeline.mp4

10:49

7. Long Context LLMs.mp4

01:44

8. Coding a Long Context LLM.mp4

09:38

9. MultiModal RAG in a few lines of code.mp4

06:03

10. Coding a Knowledge Agent with DB Memory - Part 1.mp4

09:32

11. Coding a Knowledge Agent with DB Memory - Part 2.mp4

09:15

Files.zip

4. Planning in LLM Agents

1. Why do LLMs need planning.mp4

02:49

2. Types of LLM Plans.mp4

02:30

3. Chain of Thought Prompting.mp4

09:59

4. Plan and Solve Prompting.mp4

04:45

5. Tree of Thought Planning.mp4

07:42

6. Skeleton of Thought Prompting.mp4

06:58

7. Langgraph Basics.mp4

08:40

8. Reflection Prompting.mp4

13:38

9. Reflexion Prompting.mp4

18:18

10. Planning in LLM Agents.html

Files.zip

5. Agent Examples

1. Agentic RAG pipeline explanation.mp4

05:38

2. Agentic RAG Pipeline Code.mp4

18:18

3. Movie Recommendation Bot Explanation and Tool Calls.mp4

14:05

4. Movie Recommendation Bot - Planning and Memory.mp4

16:29

5. Coding Assistant - Intro.mp4

04:19

6. Coding Assistant code walkthrough.mp4

15:06

Files.zip

Description

Master LLM Agents for Advanced Reasoning, Coding Assistants, Research Assistants, and More through Hands-On Code

What You'll Learn?

Understand the key differences between LLM prompt output, LLM Chains, Retrieval-Augmented Generation (RAG), and LLM Agents.
List and explain the main components of an LLM Agent, including tools, memory, and planning mechanisms
Utilize sophisticated task planning and reflection methods to enhance the functionality and efficiency of LLM Agents
Create and implement memory mechanisms that enable LLM Agents to sustain interaction and learn over time.
Incorporate different tools to extend and enhance the capabilities of LLM Agents.
Combine components to create complex agents such as Research Assistants, Coding Assistants, Recommendation Agents, and Agentic RAGs.
Engage in 15 interactive coding tutorials that progressively build on concepts, providing practical experience and deeper understanding.

Who is this for?

Aspiring AI specialists seeking to understand and leverage the power of LLMs

Software developers looking to innovate with Generative AI-driven applications

Data scientists aiming to broaden their expertise into AI agent design

Technologists curious about the integration of Generative AI in practical tools and services

What You Need to Know?

Basic Python Programming experience

Basic understanding of Generative AI like LLMs, writing prompts etc

More details

Description
### Course Description
Unlock the Power of Large Language Models with Our Comprehensive Course on LLM Agents!
Dive into the world of LLM Agents with our hands-on course designed to take you from basics to building sophisticated agent systems. Whether you're an AI enthusiast, a developer, or a tech professional, this course will equip you with the knowledge and skills to create powerful AI-driven agents.
What Youâ€™ll Learn:
Fundamentals of Agent Systems: Understand the core components of an agent system, including tools, memory, and planning.
Hands-On Coding: Explore each concept through interactive code notebooks, providing you with practical experience and deeper insights.
Advanced Agent Development: Build complex agents such as Research Assistants, Coding Assistants, Recommendation Agents, and Agentic RAGs, using real-world examples and scenarios.
Practical Applications: Learn how to apply these agents in various domains, enhancing productivity and innovation in your projects.
Course Highlights:
15 Interactive Code Notebooks: Each notebook is designed to break down complex concepts into manageable and understandable sections, making learning engaging and effective.
Step-by-Step Guidance: Follow along with detailed instructions and explanations, ensuring you grasp each concept before moving on.
Real-World Examples: See how these agents can be applied to solve real problems, providing you with the confidence to implement these solutions in your own work.
Community Support: Join a growing community of learners and experts, where you can share ideas, ask questions, and collaborate on projects.
By the end of this course, youâ€™ll have a solid understanding of LLM Agents and the ability to create your own customized agents for various applications. Whether youâ€™re looking to advance your career, start a new project, or simply satisfy your curiosity about AI, this course is your gateway to mastering LLM Agents.
Enroll now and start your journey into the future of AI with LLM Agents!
Who this course is for:
Aspiring AI specialists seeking to understand and leverage the power of LLMs
Software developers looking to innovate with Generative AI-driven applications
Data scientists aiming to broaden their expertise into AI agent design
Technologists curious about the integration of Generative AI in practical tools and services

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I am a seasoned AI consultant and entrepreneur with over 10 years of experience in applying deep learning and NLP to various domains and challenges. I am also an avid blogger with 10K+ followers on Medium. I love solving complex problems with ML/AI and writing to help others.Over the last couple of years, I have been fascinated with the progress in Generative AI and had the opportunity to learn through many hands on projects. I have created this course so others can learn how to build more complex LLM applications. I hold a MTech in Computer Science from Georgia Tech, and an AI Graduate certificate from Stanford

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