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Arnold Oberleiter

AI agents are on everyone's lips, but few know what they are and even fewer know how to use them.

Tools like CrewAI, Autogen, BabyAGI, LangChain, LangGraph, LangFlow etc., sound more complex than they are.

Are you ready to master the intricacies of AI agents and leverage their full potential for process automation and selling tailored solutions?

Then this course is for you.

Read more

AI agents are on everyone's lips, but few know what they are and even fewer know how to use them.

Tools like CrewAI, Autogen, BabyAGI, LangChain, LangGraph, LangFlow etc., sound more complex than they are.

Are you ready to master the intricacies of AI agents and leverage their full potential for process automation and selling tailored solutions?

Then this course is for you.

Dive into "AI Agents: Automation & Business through LangChain Apps"—where you will explore the basic and advanced concepts of AI agents and LLMs, their architectures, and practical applications. Transform your understanding and skills to lead in the AI revolution.

This course is perfect for developers, data scientists, AI enthusiasts, and anyone wanting to be at the forefront of AI agent and LLM technology. Whether you want to create AI agents, perfect their automation, or sell tailored solutions, this course provides you with the comprehensive knowledge and practical skills you need.

What to expect from this course:

Comprehensive knowledge of AI agents and LLMs:

  • Basics of AI Agents and LLMs: Introduction to AI agents like Autogen, LangChain, LangGraph, LangFlow, CrewAI, BabyAGI & their LLMs (GPT-4, Claude, Gemini, Llama & more).

  • Tools and Techniques: Using LangChain, LangGraph, and other tools to create AI agents.

  • Function Calling and Vector Databases: Understanding function calling and using vector databases and embedding models.

Creating and deploying AI agents:

  • Installation and Use of Flowise with Node: Step-by-step guides for installing and using Flowise.

  • Creating and Deploying AI Agents for Various Tasks: Developing creative writers, social media strategists, and function-calling agents.

Advanced techniques for AI agents:

  • RAG AI Agents: Training LLMs on your own data and automatic local text storage.

  • Data Preparation and Integration: Using LlamaIndex, LlamaParse, and other tools for data preparation and integration in Flowise.

  • API Connection and Automation: Connecting APIs and automating with JavaScript, Python, and Make.

AI agents in a business environment:

  • Use Cases and Integration: Hosting and integrating AI agents into websites or as standalone apps.

  • Lead Generation and Marketing: Strategies for generating leads and selling AI agents.

Creating your own AI assistant:

  • Python Code and Installation: Developing a local Microsoft Copilot-like AI agent with Vision and Python.

  • Using VS Code and Git: Step-by-step guides for installing and using VS Code and Git.

AI agents with open-source LLMs:

  • Pros and Cons of Open-Source LLMs: Using and installing open-source LLMs like Llama 3.

  • Installing and Using Ollama with Llama 3.1 and Other Open-Source LLMs.

  • Creating Open-Source AI Agents: Developing simple and advanced open-source AI agents.

Issues, security, and copyrights in AI agents:

  • Security Measures and Privacy: Understanding jailbreaks, prompt injections, and data poisoning.

  • Copyrights and Privacy: Handling copyrights and privacy for generated AI agent data.

Practical applications and API integration:

  • API Basics and Integration Skills: Using the OpenAI API, Google API, and more for various applications.

  • Developing AI Apps: Creating apps with Whisper, GPT-4, and more.

Innovative tools and agents:

  • Overview of Microsoft Autogen and CrewAI.

  • Implementing Flowise: Integrating Flowise with function calls and open-source LLMs as a chatbot.

Harness the power of AI agents and LLM technology to develop solutions and expand your understanding of their applications.

At the end of "AI Agents: Automation & Business through LangChain Apps," you will have a holistic understanding of AI agents and LLMs and the skills to use them for various purposes. If you are ready to be at the forefront of this technological revolution, this course is for you.

Enroll today and become an expert in AI agents and large language models.

Enroll now

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What's inside

Learning objectives

  • Basics of ai agents like autogen, langchain, langflow, flowise, langgraph, babyagi, crewai & more
  • Basics of llms like chatgpt, claude, gemini, llama, mistral, gpt-4o & more with function calling in llms
  • All about vector databases, embedding models & retrieval-augmented generation (rag)
  • Creating ai agents for automating content, emails, lead research & more installation and operation of flowise with node
  • Function calling for external apis, python interpreter, calculator, gmail, serper, make & more
  • Rag ai agent: training on own data & automatic saving of files on your pc
  • Data preparation for rag: pdfs, docs, csv & more with llamaindex & llamaparse
  • Integration and automation of custom tools in flowise
  • Api connection and automation with javascript, python and make
  • Ai agents in business: offering, pricing, sales, customer acquisition
  • Marketing strategies for selling ai agents
  • Integration of ai agents into websites or as standalone apps
  • Installation of vs code and git
  • Local microsoft copilot with vision as an ai agent in python
  • Ai agents with open-source llms: ollama, llama 3.1 & more
  • Choosing the right llm for the ai agent
  • Issues, security, and copyrights in ai agents
  • Show more
  • Show less

Syllabus

Introduction and Overview
Welcome
Course Overview
My Goal and a Few Tips
Read more
Explanation of the Links
Important Links
Basics: AI Agents, LLMs, Function Calling, Vector Databases & Embeddings
What This Section is About
What are AI-Agents? A quick overview
What are LLMs like ChatGPT, Claude, Gemini, Llama, Mistral etc.
What is Function Calling in LLMs?
Vector Databases, Embedding Models & Retrieval-Augmented Generation (RAG)
AI Agents explained & Tools like Autogen, LangChain, LangGraph, CrewAI & more
What is a API? Function calling for AI-Agents with APIs
Recap: What You Have Learned So Far
Creating Your First AI Agents
What Will You Learn in This Section?
Running Flowise Locally with Node.js: Installing Node
Installing Flowise with Node.js via Command Prompt
The Flowise Interface: LangChain/LangGraph made Easy
Our First AI Agent: Boss, Creative Writer & Title Generator
AI Agent No. 2: Social Media Strategy & Prompt Engineering for AI Agents
AI Agent No. 3: Function Calling, Lead Research on the Web & Personal Emails
Agent 4: Function Calling, Python Interpreter, Calculator & Local Text Storage
Summary: Important Points You Should Not Forget
Advanced AI Agents: RAG, Custom Tools & Actions in Apps
What is This Section About?
RAG AI Agent: Training LLMs on Your Data & Automatic Content Storage
Tips for Better RAG Apps: Firecrawl for Your Web Data
RAG with LlamaIndex & LlamaParse: Data Preparation for PDFs, Docs, CSV & More
LlamaIndex brings easy LlamaParse!
Chunk Size and Chunk Overlap for a Better RAG Application
Overview of Custom Tools in Flowise
Connect any API to Flowise with Custom Tools & JavaScript Functions
Custom Tools and Automation of Gmail with Make (Part. 1)
Automation of Gmail with Make scenarios, Webhooks, & Google API (Part 2)
Recap: What You Have Learned and Mistakes to Avoid
AI Agents for Business: Hosting, Lead Generation & Sales
What Will You Learn Here?
Applications of AI Agents in Business
Example of a Simple AI-Agent then we can sell
External Hosting of Chatbots for Clients (or for us) on Render
Integrating AI Agents in Websites or Using Them as Standalone Apps
Making Standalone Apps More Appealing
Visually Improving Chatbots on Websites: Branding, Style & Integrating Links
Generating Leads, Integrating Audio Models & Additional Functions
Selling AI Agents: Marketing, Customer Acquisition, Offer, Sales & Warranty
Summary: Important Points to Remember!
Creating Your Own AI Assistant, Similar to Microsoft Copilot
What Will We Learn in This Section?
Overview of the Python Code on Github
Installing Visual Studio Code (VS Code) for Python, Javascript & more
Installing Git for Projects from GitHub
Our Project: Microsoft Copilot with Vision as Our Own AI Agent (In Python)
Additional Tips, Use Cases, Different Voices & Prompts
Security, API Costs, Speed & Hardware
Copy my Python Code (if you like)
Code & Requirements for Desktop Recording (Simple)
Recap What You Should Not Forget
AI Agents with Open-Source LLMs: Private & Uncensored AI on Your PC
Pros and Cons of Open-Source LLMs like Llama3.1, Mistral & More
Installing Ollama and Downloading Open-Source LLMs
A Simple Open-Source AI Agent with Llama 3.1 & Ollama (LangChain/LangGraph)
Advanced Open-Source AI Agent with Llama 3.1: Responding to Emails
Local RAG Chatbot with Flowise, Llama3 & Ollama: A Local Langchain App
Insanely fast inference with the Groq API
Llama 3.1: Infos and What Models should you use?
Important Points to Remember
Issues, Security, and Copyrights in AI Agents
What Will We Learn in This Section
Jailbreaks: A Method to Hack LLMs with Prompts
Prompt Injections: Another Security Vulnerability of LLMs
Data Poisoning and Backdoor Attacks
Copyrights & Intellectual Property of Generated Data from AI Agents
Privacy & Protection for your own and Client Data
Recap: Important Points to Remember!
What’s Next?
What’s Next and My Thank You!
Bonus

Good to know

Know what's good
, what to watch for
, and possible dealbreakers
Explores industry-standard LLMs like GPT-4, Claude, Gemini, and Llama
Develops foundational knowledge and skills in AI agents and LLMs
Provides hands-on experience in creating, deploying, and integrating AI agents for various applications
Covers advanced techniques for AI agents, including RAG, data preparation, and API integration
Focuses on business applications of AI agents, including lead generation, marketing, and sales
Taught by Arnold Oberleiter, an expert in AI and LLM technology
Teaches Python and JavaScript, essential programming languages for AI agent development
Explores ethical considerations and potential issues in AI agent development, such as security and copyright

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Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in AI-Agents: Automation & Business with LangChain & LLM Apps with these activities:
Function Calling in LLMs
Function calling in LLMs is very useful and versatile. This practice drill is designed to help you develop proficiency with this technique, ensuring your confidence in using it during your AI agent development.
Browse courses on Function Calling
Show steps
  • Review the documentation on function calling in LLMs.
  • Implement function calling in your own AI agent code.
  • Test your AI agent to ensure that the function calling is working correctly.
Show all one activities

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