Udemy – Become An Llm & Agentic Ai Engineer: 14-Day Bootcamp – 2025 2025-3

Udemy – Become An Llm & Agentic Ai Engineer: 14-Day Bootcamp – 2025 2025-3 Downloadly IRSpace

Aug 16, 2025 - 07:21 Updated: Aug 17, 2025 - 19:58

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Udemy – Become An Llm & Agentic Ai Engineer: 14-Day Bootcamp – 2025 2025-3

Become An LLM & Agentic Ai Engineer: 14-Day Bootcamp – 2025. The AI revolution is accelerating at an unprecedented pace, and those who master Large Language Models (LLMs) and Agentic AI will shape the future of technology. The Become An LLM & Agentic AI Engineer Bootcamp is an intensive, hands-on 14-day program designed to equip professionals and enthusiasts with the skills needed to build real-world AI applications. Whether you’re a developer, data scientist, researcher, or technology leader, this bootcamp provides the tools and knowledge to navigate and innovate confidently in this rapidly evolving space. Participants begin by exploring the fundamentals of LLMs and agentic frameworks, including how to benchmark models using LM Studio. The course then guides them through working with powerful closed APIs from providers like OpenAI, Gemini, and Claude. They learn how to structure system and user messages, understand tokenization, and control outputs to build projects like AI-powered text generators and vision-enabled calorie trackers.

As they progress through the course, they enter the world of open source LLMs. They fine-tune models in Hugging Face using advanced techniques such as LoRA and Parameter Efficient Fine Tuning (PEFT). Along with this, they gain experience designing AI-based web applications using Gradio, creating interactive broadcast applications, and building intelligent AI educational systems. A major part of the bootcamp focuses on mastering Prompt Engineering, including Zero-Shot, Few-Shot, and Chain-of-Thought prompting techniques to achieve consistent and controlled outputs. They also explore advanced capabilities such as building Retrieval Augmented Generation (RAG) pipelines and working with Embeddings for semantic search and knowledge retrieval.

What you will learn

Understand the principles of large language models (LLMs) and agent-based AI, including how to train, fine-tune, and deploy LLMs.
They will create and deploy autonomous and intelligent AI agents using advanced frameworks such as AutoGen, OpenAI Agents SDK, LangGraph, n8n, and MCP.
Open source LLMs such as LLama, DeepSeek, Qwen, Phi, and Gemma will be explored and benchmarked using Hugging Face and LM Studio.
They will develop real-world applications using API access to OpenAI, Gemini, and Claude for text generation and vision tasks.
They will apply a proven 5-step framework to select the right AI model for their business: maximize cost efficiency, minimize latency, and accelerate time to market.
They will evaluate LLMs using leaderboards such as Vellum and Chat Arena and conduct blind tests to objectively assess the performance of the AI model.
They will design Retrieval Augmented Generation (RAG) pipelines using LangChain, OpenAI Embeddings, and ChromaDB to efficiently retrieve documents and answer questions.
They will build an interactive and transparent AI-powered Q&A system with the Gradio interface that displays answers along with source references to increase user trust.
They will learn to validate data and generate structured output using the Pydantic library, including BaseModel, Type Hints, and generate parsed output from OpenAI models.
They will build an AI-powered resume editor that analyzes the gap between resumes and job descriptions and automatically tailors resumes/cover letters to targeted applications.
They will learn how to fine-tune pre-trained open source LLMs using efficient parametric methods such as LoRA and tools such as TRL and SFTTrainer from Hugging Face.
They will learn techniques for data set preparation and model evaluation, including calculating precision, accuracy, recall, and F1 score using scikit-learn.
Key components in the Hugging Face Transformers library will be implemented such as pipeline(), AutoTokenizer(), and AutoModelForCausalLM().
They will gain hands-on experience working with open source datasets/models in Hugging Face and will apply quantization techniques such as bitsandbytes to optimize performance.
They will learn advanced prompt engineering techniques such as zero-shot, multi-shot, and chain-of-thought prompting.
Agents will deploy multi-model AI using AutoGen, integrating LLMs from OpenAI, Gemini, and Claude, enabling agent collaboration and human supervision in the loop.
They will develop and deploy AI agent workflows using LangGraph and master concepts such as states, edges, conditional logic, and multi-stage nodes.
And…

This course is suitable for people who:

They have a laptop and an internet connection.
No programming experience; basic Python skills are an advantage.
There are software engineers, data scientists, AI researchers, and technology professionals who want to enter the field of LLM and AI agent development.

Course Details Calculus I Calculus II Calculus III – A Complete Course

Publisher: Udemy
Lecturer: Dr. Ryan Ahmed 400K+ Students | Best-Selling Professor 250K+ YouTubers
Training level: Beginner to advanced
Training duration: 24 hours and 7 minutes
Number of lessons: 298

Course headings

1 – Welcome to the Bootcamp
2 – PART A CLOSEDSOURCE LLMs GRADIO BENCHMARKING
3 – Day 1 Develop a Character AI Chatbot Using OpenAI API
4 – Day 2 Build an AI Calorie Tracker Using OpenAI API Vision GPTs
5 – Day 3 Build an Adaptive LLMAI Tutor with Gradio for Multilevel Learning
6 – Day 4 Build Websites with Claude Gemini OpenAI LLMs Leaderboards
7 – PART B OPENSOURCE LLMs HUGGING FACE RAG FINETUNING
8 – Day 5 Hugging Face OpenSource Models
9 – Day 6 Reasoning OpenSource LLMs on Hugging Face Model Leaderboards
10 – Day 7 Build Retrieval Augmented Generation RAG Pipelines in LangChain
11 – Day 8 Build a Resume Cover Letter AI Assistant with OpenAI Pydantic
12 – Day 9 FineTuning of Large Language Models with LORA SFTTrainer PEFT TRL
13 – PART C AI AGENTS WITH LANGGRAPH AUTOGEN CREWAI N8N MCP
14 – Day 10 Build MultiModel AI Agent Teams Using AutoGen
15 – Day 11 Building AI Agentic Workflows in LangGraph
16 – Day 12 Build A Team of Data Science AI Agents Using CrewAI
17 – Day 13 Build Agentic AI Workflows in n8n
18 – Day 14 Build AI Agents with Model Context Protocol MCP OpenAI Agents SDK
19 – Congratulations and Thank You

Udemy – Become An Llm & Agentic Ai Engineer: 14-Day Bootcamp – 2025 2025-3