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.
Read more
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.
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
- Show more
- Show less
- 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
Read more
Syllabus
Traffic lights
Save this course
Save
Reviews summary
Comprehensive llm mastery for professionals
Directly relevant for career advancement in AI and data science roles.
Explores a wide range of current LLMs, including open-source and closed-source models.
Provides a broad and deep understanding of LLM fundamentals and advanced topics.
Focuses heavily on practical application, API usage, and AI app development.
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
Show steps
-
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
Show steps
Enhance your practical skills by building an AI app that leverages LLMs.
Show steps
-
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
Show steps
-
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
Show steps
Gain hands-on experience and contribute to the advancement of LLM technology.
Show steps
-
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.
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
Show steps
- 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.
Develop an LLM-Powered App
Show steps
Enhance your practical skills by building an AI app that leverages LLMs.
Show steps
- 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
Show steps
- 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
Show steps
Gain hands-on experience and contribute to the advancement of LLM technology.
Show steps
- 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
Featured in The Course Notes
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