𝐇𝐨𝐰 𝐭𝐨 𝐁𝐞𝐠𝐢𝐧 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠 𝐀𝐈 𝐀𝐠𝐞𝐧𝐭𝐬 🔹 𝐋𝐞𝐯𝐞𝐥 𝟏: 𝐅𝐨𝐮𝐧𝐝𝐚𝐭𝐢𝐨𝐧𝐬 𝐨𝐟 𝐆𝐞𝐧𝐀𝐈 𝐚𝐧𝐝 𝐑𝐀𝐆 ▪️ Introduction to Generative AI (GenAI): Understand the basics of Generative AI, its key use cases, and why it's important in modern AI development. ▪️ Large Language Models (LLMs): Learn the core principles of large-scale language models like GPT, LLaMA, or PaLM, focusing on their architecture and real-world applications. ▪️ Prompt Engineering Fundamentals: Explore how to design and refine prompts to achieve specific results from LLMs. ▪️ Data Handling and Processing: Gain insights into data cleaning, transformation, and preparation techniques crucial for AI-driven tasks. 🔹 𝐋𝐞𝐯𝐞𝐥 𝟐: 𝐀𝐝𝐯𝐚𝐧𝐜𝐞𝐝 𝐂𝐨𝐧𝐜𝐞𝐩𝐭𝐬 𝐢𝐧 𝐀𝐈 𝐀𝐠𝐞𝐧𝐭𝐬 ▪️ API Integration for AI Models: Learn how to interact with AI models through APIs, making it easier to integrate them into various applications. ▪️ Understanding Retrieval-Augmented Generation (RAG): Discover how to enhance LLM performance by leveraging external data for more informed outputs. ▪️ Introduction to AI Agents: Get an overview of AI agents—autonomous entities that use AI to perform tasks or solve problems. ▪️ Agentic Frameworks: Explore popular tools like LangChain or OpenAI’s API to build and manage AI agents. ▪️ Creating Simple AI Agents: Apply your foundational knowledge to construct a basic AI agent. ▪️ Agentic Workflow Overview: Understand how AI agents operate, focusing on planning, execution, and feedback loops. ▪️ Agentic Memory: Learn how agents retain context across interactions to improve performance and consistency. ▪️ Evaluating AI Agents: Explore methods for assessing and improving the performance of AI agents. ▪️ Multi-Agent Collaboration: Delve into how multiple agents can collaborate to solve complex problems efficiently. ▪️ Agentic RAG: Learn how to integrate Retrieval-Augmented Generation techniques within AI agents, enhancing their ability to use external data sources effectively. Join for more AI Resources: https://t.me/machinelearning_deeplearning