LLM Mastery: ChatGPT, Gemini, Claude, Llama3, OpenAI & APIs from Udemy

Have you ever thought about how Large Language Models (LLMs) are transforming the world and creating unprecedented opportunities?

"AI won't take your job, but someone who knows how to use AI might," says Richard Baldwin.

Are you ready to master the intricacies of LLMs and leverage their full potential for various applications, from data analysis to the creation of chatbots and AI agents?

Then this course is for you.

Dive into 'LLM Mastery: ChatGPT, Gemini, Claude, Llama3, OpenAI & APIs'—where you will explore the fundamental and advanced concepts of 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 who wants to be at the forefront of LLM technology. Whether you want to understand neural networks, fine-tune AI models, or develop AI-driven applications, this course offers everything you need.

What to expect in this course:

Comprehensive Knowledge of LLMs:

Understanding LLMs: Learn about parameters, weights, inference, and neural networks.
Neural Networks: Understand how neural networks function with tokens in LLMs.
Transformer Architecture: Explore the Transformer architecture and Mixture of Experts.
Fine-Tuning: Understand the fine-tuning process and the development of the Assistant model.
Reinforcement Learning (RLHF): Dive into reinforcement learning with human feedback.

Advanced Techniques and Future Trends:

Scaling Laws: Learn about the scaling laws of LLMs, including GPU and data improvements.
Future of LLMs: Discover the capabilities and future developments in LLM technology.
Multimodal Processing: Understand multimodality and visual processing with LLMs, inspired by movies like "Her."

Practical Skills and Applications:

Tool Utilization: Use tools with LLMs like calculators and Python libraries.
Systems Thinking: Dive into systems thinking and future perspectives for LLMs.
Self-Improvement: Learn self-improvement methods inspired by AlphaGo.
Optimization Techniques: Enhance LLM performance with prompts, RAG, function calling, and customization.

Prompt Engineering:

Advanced Prompts: Master techniques like Chain of Thought and Tree of Thoughts prompting.
Customization: Customize LLMs with system prompts and personalize with ChatGPT memory.
Long-Term Memory: Implement RAG and GPTs for long-term memory capabilities.

API and Integration Skills:

API Basics: Understand the basics of API usage, including OpenAI API, Google Gemini, and Claude APIs.
Microsoft and GitHub Copilot: Utilize Microsoft Copilot in 365 and GitHub Copilot for programming.
OpenAI API Mastery: Explore functionalities, pricing models, and app creation with the OpenAI API.

AI App Development:

Google Colab: Learn API calls to OpenAI with Google Colab.
AI Agents: Create AI agents for various tasks in LangChain frameworks like Langgraph, Langflow, Vectorshift, Autogen, CrewAI, Flowise, and more.
Security: Ensure security with methods to prevent jailbreaks and prompt injections.

Comparative Insights:

Comparing Top LLMs: Compare the best LLMs, including Google Gemini, Claude, and more.
Open-Source Models: Explore and utilize open-source models like Llama 3, Mixtral, and Command R+ with the possibility of running everything locally on your PC for maximum security.

Practical Applications:

Embedding and Vector Databases: Implement embeddings for RAG.
Zapier Integration: Integrate Zapier actions into GPTs.
Open-Source LLMs: Install and use LM Studio for local open-source LLMs for maximum security.
Model Fine-Tuning: Fine-tune open-source models with Huggingface.
API-Based App Development: Create apps with DALL-E, Whisper, GPT-4o, Vision, and more in Google Colab.

Innovative Tools and Agents:

Microsoft Autogen: Use Microsoft Autogen for developing AI agents.
CrewAI: Develop AI agents with CrewAI.
LangChain: Understand the framework with divisions like LangGraph, LangFlow, and more.
Flowise: Implement Flowise with function calls and open-source LLM as a chatbot.

Ethical and Security Considerations:

LLM Security: Understand and apply security measures to prevent hacking.
Future of LLMs: Explore the potential of LLMs as operating systems in robots and PCs.

This course is ideal for anyone looking to delve deeper into the world of LLMs—from developers and creatives to entrepreneurs and AI enthusiasts.

Harness the transformative power of LLM technology to develop innovative solutions and expand your understanding of their diverse applications.

By the end of 'LLM Mastery: ChatGPT, Gemini, Claude, Llama3, OpenAI & APIs' you will have a comprehensive understanding of LLMs, their applications, and the skills to harness their power for various purposes. If you are ready to embark on a transformative journey into AI and position yourself at the forefront of this technological revolution, this course is for you.

Enroll today and start your journey to becoming an expert in the world of Large Language Models.

What's inside

Learning objectives

Functionality of llms: parameters, weights, inference, and neural networks
Understanding neural networks
Operation of neural networks with tokens in llms
Transformer architecture and mixture of experts
Fine-tuning and the creation of the assistant model
Reinforcement learning (rlhf) in llms
Llm scaling laws: gpu & data for improvements
Capabilities and future developments of llms
Use of tools by llms: calculator, python libraries, and more
Multimodality and visual processing with llms
Multimodality in language as in the movie 'her'
Systems thinking and future prospects for llms
Self-improvement after alphago (self-improvement)
Improvement possibilities: prompts, rag, and customization
Prompt engineering: effective use of llms with chain of thought and tree of thoughts prompting & more
Adaptation of llms through system prompts and personalization with chatgpt memory
Long-term memory with rag and gpts
The gpt store: everything you need to know
Using gpts for data analysis, pdfs, or tetris programming
Embeddings and vector databases for rag
Integrating zapier actions in gpts
Open-source vs. closed-source llms
Api basics
Usage of the google gemini api and claude api
Microsoft copilot and its use in microsoft 365
Github copilot: the solution for programmers
The openai api: features, pricing models, and everything you need to know about the openai api including app creation

Introduction to google colab for api calls to openai
Creation of ai apps and chatbots with langchain, flowise, vectorshift, langgraph, crewai, autogen, langflow & more
Creation of ai agents for various tasks like social media contetn with agency swarm and langchain agents
Security in llms: jailbreaks and prompt injections & more
Comparison of the best llms
Google gemini in standard interface and google labs with notebooklm
Claude by anthropic: overview
Everything about perplexity and poe
Openai playground: features, billing account & temperature of llms
Google gemini api: video analysis and more
Open-source llms: models and use of llama 3, mixtral, command r+, and many more
Huggingchat: interface for open-source llms
Running local llms with ollama and building local rag chatbots
Groq: fastest interface with lpu
Installation of lm studio for using local open-source like llama3 llms for maximum security
Using open-source models in lm studio and censored vs. uncensored llms
Fine-tuning an open-source model with huggingface
Creating your own apps via apis in google colab with dall-e, whisper, gpt-4o, vision, and more
Microsoft autogen for ai agents
Crewai for ai agents
Flowise with langchain function calling
Openai assistant api with function calling for ai-agents in different frameworks
Flowise with open-source llm as chatbot
Security in llms and methods to hack llms
Future of llms as operating systems in robots and pcs
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Functionality of llms: parameters, weights, inference, and neural networks
Understanding neural networks
Operation of neural networks with tokens in llms
Transformer architecture and mixture of experts
Fine-tuning and the creation of the assistant model
Reinforcement learning (rlhf) in llms
Llm scaling laws: gpu & data for improvements
Capabilities and future developments of llms
Use of tools by llms: calculator, python libraries, and more
Multimodality and visual processing with llms
Multimodality in language as in the movie 'her'
Systems thinking and future prospects for llms
Self-improvement after alphago (self-improvement)
Improvement possibilities: prompts, rag, and customization
Prompt engineering: effective use of llms with chain of thought and tree of thoughts prompting & more
Adaptation of llms through system prompts and personalization with chatgpt memory
Long-term memory with rag and gpts
The gpt store: everything you need to know
Using gpts for data analysis, pdfs, or tetris programming
Embeddings and vector databases for rag
Integrating zapier actions in gpts
Open-source vs. closed-source llms
Api basics
Usage of the google gemini api and claude api
Microsoft copilot and its use in microsoft 365
Github copilot: the solution for programmers
The openai api: features, pricing models, and everything you need to know about the openai api including app creation
Introduction to google colab for api calls to openai
Creation of ai apps and chatbots with langchain, flowise, vectorshift, langgraph, crewai, autogen, langflow & more
Creation of ai agents for various tasks like social media contetn with agency swarm and langchain agents
Security in llms: jailbreaks and prompt injections & more
Comparison of the best llms
Google gemini in standard interface and google labs with notebooklm
Claude by anthropic: overview
Everything about perplexity and poe
Openai playground: features, billing account & temperature of llms
Google gemini api: video analysis and more
Open-source llms: models and use of llama 3, mixtral, command r+, and many more
Huggingchat: interface for open-source llms
Running local llms with ollama and building local rag chatbots
Groq: fastest interface with lpu
Installation of lm studio for using local open-source like llama3 llms for maximum security
Using open-source models in lm studio and censored vs. uncensored llms
Fine-tuning an open-source model with huggingface
Creating your own apps via apis in google colab with dall-e, whisper, gpt-4o, vision, and more
Microsoft autogen for ai agents
Crewai for ai agents
Flowise with langchain function calling
Openai assistant api with function calling for ai-agents in different frameworks
Flowise with open-source llm as chatbot
Security in llms and methods to hack llms
Future of llms as operating systems in robots and pcs
Show more
Show less

Syllabus

Introduction and Overview

Welcome

Course Overview

My Goal and Some Tips

Explanation of Links and Downloads

Important Links

How LLMs Work: Parameters, Weights, Inference, Neural Networks & More

What This Section Is About?

An LLM Consists of Only Two Files Parameter File and a Few Lines of Code

How Are the Parameters Created Pretraining (Initial Training of the LLM)

What Is a Neural Network and how it works?

How a Neural Network Works in an LLM with Tokens

The Transformer Architecture Is Not Fully Understood (Yet?)

Other Possibilities of the Transformer Architecture: Mixture of Experts Explaied

After Pretraining Comes Finetuning: The Assistant Model Is Created

The Final Step: Reinforcement Learning (RLHF)

LLM Scaling Laws: To Improve LLM, We Only Need Two Things, GPU & Data

Review: What Have You Learned So Far

Additional Capabilities of LLMs & Future Developments

What This Section Is About

LLMs Can Use Various Tools, Like Calculators, Python Libraries, etc.

Multimodality, Visual Processing (Vision), and Image Recognition

Multimodality with Language Like in the Movie "Her"

What Could Happen in the Future? Systems Thinking! [Thinking Fast and Slow]

Self-Improvement Inspired by AlphaGo

Further Ways to Improve LLMs: Prompts, RAG, Customization/System Prompts

LLMs as the New Operating System: What the Future Could Look Like

Review: What Have You Learned in This Section

Prompt Engineering: Effective Use of LLMs in the Standard Interface

What This Section Is About and the Interface of LLMs

What is the Token Limit and why is it important

Why Is Prompt Engineering Important? An Example!

Prompt Engineering Basics: Semantic Association

Prompt Engineering for LLMs: The Simplest Strategies (Structured Prompts)

3 Important "hacks" for Prompt Engineering and the Instruction Pormpting

Role Prompting in ChatGPT and other LLMs

Shot Prompting: Zero-Shot, One-Shot und Few-Shot

Reverse Prompt Engineering and the "OK" Trick

Chain of Thought Prompting: Step by Step to the Goal

Tree of Thoughts (ToT) Prompting

The Combination of Prompting Concepts

Real-World Use Cases for Large Language Models

Review and a bit of Homework

LLM Customization: System Prompts, Memory, RAG & Creating Expert Models or GPTs

The Simplest Form of Personalization: ChatGPT Memory

Customization Through System Prompts and Custom Instructions

In-Context Learning: Short-Term Memory as Simple as Possible

In-Context Learning: "The Short-Term Memory" but Efficient with SPR

Embeddings and Vector Databases for RAG: A Detailed Explanation

Long-Term Memory with RAG: As Simple as Possible with GPTs & RAG

The GPT Store: Everything You Need to Know & Testing of GPTs for Code, PDFs & YT

Three ways to make Money with GPTs

First: You need a Builder Profile to generate Leads from GPTs

Create a GPT with Knowledge that can generate Leads and makes Upsells

What is a API?

Zapier Actions in GPTs: Automate Gmail, Google Docs, & more with the Zapir API

How to Integrate Every API in your GPT

Summary: What You Have Learned in This Section

Closed-Source LLMs: An Overview of Available Models and how to use them

Open-Source vs. Closed-Source LLMs

What is the difference: Parameters, Architecture, Pretraining size & more

Google Gemini in the Standard Interface: Everything you need to know

Google Labs with NotebookLM: The Best Method to Learn Books

Claude by Anthropic: An Overview

The Leading Companies Are OpenAI, Google & Anthropic: Many Are Building on Them

Perplexity: Advantages and Disadvantages, and Applications

Poe, The Versatile All-in-One Platform

What is the Microsoft Copilot: How it works and is my Data Save?

Using Microsoft Copilot in the Web Interface

Microsoft Copilot PCs

Microsoft 365: Differences Between Free and Paid Subscription

The Right Copilot Subscription and a Free Alternative.

Copilot in Microsoft Word: Write Faster Than Ever

Copilot in Microsoft PowerPoint: The Quick Presentation

Copilot in Microsoft Outlook: Write and Reply to Your Emails Faster

Copilot in Microsoft Excel: Big Possibilities but Still a Bit Early

Microsoft Copilot GPT: Create your own personalized ChatBots

GitHub Copilot: The AI Solution for Programmers

Conclusion on Microsoft Copilot

Review of the Closed-Source LLMs

APIs of Closed-Source LLMs

What Is This About? APIs of Closed-Source LLMs

Overview of the OpenAI API

Pricing Models of the OpenAI API

Important: OpenAI Playground overview and Billing Account

The OpenAI Playgroundin action

The Google Gemini API: Video Analysis and Other Features

The Anthropic API for the Claude Models

Summary of the Closed-Source APIs

Open-Source LLMs: Available Models and Their Use in the Claude & Locally

What Are Open-Source LLMs and Which Ones Are Available

Huggingface: An Introduction

HuggingChat: An Interface for Using Open-Source LLMs with Function Calling

Groq: The Fastest Interface with an LPU Instead of a GPU

Installation of LM Studio for Opensource LLMs: You need GPU, CPU, Cuda, Ram

Using Open-Source Models in LM Studio: Llama3, Mistral; Phi-3 & more

Censored vs. Uncensored LLMs (Llama3 Dolphin)

Setting Up Your Own Local Server with LM Studio

Finetuning an Open-Source Model with Huggingface or Google Colab

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

This course is designed for Python developers who want to master the art of Python web development and become full-stack developers

Explores RESTful APIs, MongoDB, and deployment on Heroku, providing a well-rounded foundation for building sophisticated web applications

Taught by experienced instructors who are working professionals in the field, ensuring that the course content is up-to-date and industry-relevant

Provides hands-on, project-based learning, allowing students to apply their knowledge and build a portfolio of web development projects

Students will learn the fundamentals of Python programming, including data types, control flow, functions, and object-oriented programming

Reviews HTML, CSS, and JavaScript, ensuring that students have a solid understanding of the core technologies used in web development

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 LLM Mastery: ChatGPT, Gemini, Claude, Llama3, OpenAI & APIs with these activities:

Connect with LLM Experts

Show steps

Seek guidance and insights from experienced professionals in the field of LLMs.

Show steps

Identify potential mentors who have expertise in LLM development or applications.
Reach out to mentors via email, LinkedIn, or other platforms.
Request guidance, advice, or support on specific LLM-related topics.

Explore LLM APIs and Integrations

Show steps

Expand your knowledge of LLM APIs and their integration possibilities.

Browse courses on OpenAI API

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Review the documentation and tutorials for the OpenAI API and other LLM APIs.
Experiment with different API calls and parameters to understand their functionality.
Explore integrations with Microsoft Copilot and GitHub Copilot.

Practice Prompt Engineering Techniques

Show steps

Master the art of crafting effective prompts to maximize LLM output.

Browse courses on Prompt Engineering

Show steps

Study different prompt engineering techniques, such as chain-of-thought and tree-of-thoughts prompting.
Practice writing prompts for various tasks, such as content generation, code completion, or question answering.
Experiment with different prompt structures and variations to improve results.

Three other activities

Expand to see all activities and additional details

Show all six activities

Develop an LLM-Powered App

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Enhance your practical skills by building an AI app that leverages LLMs.

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Identify a problem or opportunity that an LLM-powered app can solve.
Choose an appropriate LLM and API.
Design and develop your app using Google Colab or other tools.
Test and iterate on your app to improve its performance.

Gather Resources on LLM Security and Ethical Considerations

Show steps

Enhance your awareness of potential risks associated with LLMs and develop strategies to mitigate them.

Browse courses on Ethical Considerations

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Research best practices for securing LLM-based applications and data.
Identify potential biases and ethical concerns related to LLM usage.
Compile a list of resources, articles, and guidelines on LLM security and ethics.

Contribute to Open-Source LLM Projects

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Gain hands-on experience and contribute to the advancement of LLM technology.

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Identify open-source LLM projects that align with your interests and skills.
Review the project documentation and codebase.
Identify areas where you can contribute, such as bug fixes, feature enhancements, or documentation improvements.
Submit your contributions to the project repository.

Career center

Learners who complete LLM Mastery: ChatGPT, Gemini, Claude, Llama3, OpenAI & APIs will develop knowledge and skills that may be useful to these careers:

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LLM Mastery

ChatGPT, Gemini, Claude, Llama3, OpenAI & APIs

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What's inside

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