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Unclaimed ProfileLLM Mastery serves as a comprehensive gateway into the rapidly evolving landscape of generative artificial intelligence, bridging the gap between basic prompting and advanced AI agent development. This course provides a deep dive into the technical underpinnings of Large Language Models, including transformer architecture, reinforcement learning from human feedback, and neural network functioning. Beyond theoretical knowledge, learners engage with the practical application of industry-leading tools and frameworks such as LangChain, CrewAI, and AutoGen. The curriculum is meticulously structured to guide users through the entire lifecycle of AI deployment—from setting up local environments with Ollama and LM Studio to building sophisticated RAG pipelines using vector databases. Whether you are aiming to leverage proprietary APIs like OpenAI and Google Gemini or looking to explore the privacy-conscious world of open-source models like Llama 3 and Mixtral, this program offers the technical depth required to build resilient AI applications. By mastering function calling, agentic workflows, and system integration, students are empowered to move beyond simple chat interfaces and architect autonomous agents capable of complex task execution, data analysis, and workflow automation in real-world professional environments.
About the creator
Arnold Oberleiter
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Arnold Oberleiter is a technical educator and developer specializing in cutting-edge AI technologies, autonomous agents, and practical automation tools. His instructional style bridges the gap between complex theoretical concepts and direct, hands-on application. He specializes in breaking down advanced paradigms—such as LLMs (ChatGPT, Gemini, Claude), local deployment of diffusion models, agentic workflows with n8n and LangChain, and integrating AI into daily productivity, investing, and development. By focusing on real-world utility, Arnold guides his students through building tangible local AI architectures, securing autonomous agent installations, and implementing API integrations. He teaches with a pragmatic, project-driven approach, designed to strip away the…Show more
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Program Overview
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What You'll Learn
- 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
- 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
Best For
- Software developers looking to integrate LLM capabilities into enterprise applications.
- Data scientists interested in building RAG pipelines and custom AI agents.
- Tech-savvy professionals who want to move beyond basic ChatGPT usage to API-driven automation.
- AI enthusiasts who want to learn how to run and fine-tune open-source models locally.
Not For
- Individuals seeking a non-technical introduction to prompting without coding involvement.
- Users looking for a high-level marketing overview of AI tools rather than implementation details.
- Beginners who lack basic familiarity with Python or API structures.
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