Get AI Engineer Core Track: LLM Engineering, RAG, QLoRA, Agents with 90% OFF Udemy Coupon
Become an LLM Engineer in 8 weeks: Build and deploy 8 LLM apps, mastering Generative AI, RAG, LoRA and AI Agents.
Key Takeaways — Course Overview
The following summarizes all verified data points for AI Engineer Core Track: LLM Engineering, RAG, QLoRA, Agents, including pricing, duration, instructor, and coupon validity. All data is sourced directly from Udemy and verified by CoursesWyn on .
Course Title: AI Engineer Core Track: LLM Engineering, RAG, QLoRA, Agents
Platform: Udemy (listed via CoursesWyn)
Instructor: Ed Donner, Ligency
Coupon Verified:
Difficulty Level: All Levels
Category: Development
Subcategory: Large Language Models (LLM)
Duration: 41h 30m of on-demand video
Language: English
Access: Lifetime access to all course lectures and updates
Certificate: Official certificate of completion issued by Udemy upon finishing all course requirements
Top Learning Outcomes: Students who complete AI Engineer Core Track: LLM Engineering, RAG, QLoRA, Agents will be able to: Project 1: Make AI-powered brochure generator that scrapes and navigates company websites intelligently. · Project 2: Build Multi-modal customer support agent for an airline with UI and function-calling. · Project 3: Develop Tool that creates meeting minutes and action items from audio using both open- and closed-source models.
Prerequisites: Familiarity with Python. This course will not cover Python basics and is completed in Python.
Price: $10.99 with coupon / Regular Udemy price: $109.99. Applying this coupon saves you $99.00 (90% OFF).
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What You'll Learn
Completing AI Engineer Core Track: LLM Engineering, RAG, QLoRA, Agents gives you the following verified skills and competencies in Development:
- Project 1: Make AI-powered brochure generator that scrapes and navigates company websites intelligently.
- Project 2: Build Multi-modal customer support agent for an airline with UI and function-calling.
- Project 3: Develop Tool that creates meeting minutes and action items from audio using both open- and closed-source models.
- Project 4: Make AI that converts Python code to optimized C++, boosting performance by 60,000x!
- Project 5: Build AI knowledge-worker using RAG to become an expert on all company-related matters.
- Project 6: Capstone Part A – Predict product prices from short descriptions using Frontier models.
- Project 7: Capstone Part B – Execute Fine-tuned open-source model to compete with Frontier in price prediction.
- Project 8: Capstone Part C – Build Autonomous multi agent system collaborating with models to spot deals and notify you of special bargains.
- Compare and contrast the latest techniques for improving the performance of your LLM solution, such as RAG, fine-tuning and agentic workflows
- Weigh up the leading 10 frontier and 10 open-source LLMs, and be able to select the best choice for a given task
Requirements
The following background knowledge and tools are recommended before starting AI Engineer Core Track: LLM Engineering, RAG, QLoRA, Agents. Students without these prerequisites may still enroll but should expect a steeper learning curve.
- Familiarity with Python. This course will not cover Python basics and is completed in Python.
- A PC with an internet connection is required. Either Mac (Linux) or Windows.
- We recommend that you allocate around $5 for API costs to work with frontier models. However, you can complete the course using open-source models if you prefer.
About This Udemy Course
The following is the full official course description for AI Engineer Core Track: LLM Engineering, RAG, QLoRA, Agents as published on Udemy by instructor Ed Donner, Ligency . It covers the curriculum structure, teaching methodology, and topic scope for this Development course.
- Build advanced Generative AI products using cutting-edge models and frameworks.
- Experiment with over 20 groundbreaking AI models, including Frontier and Open-Source models.
- Develop proficiency with platforms like HuggingFace, LangChain, and Gradio.
- Implement state-of-the-art techniques such as RAG (Retrieval-Augmented Generation), QLoRA fine-tuning, and Agents.
- Create real-world AI applications, including:
- A multi-modal customer support assistant that interacts with text, sound, and images.
- An AI knowledge worker that can answer any question about a company based on its shared drive.
- An AI programmer that optimizes software, achieving performance improvements of over 60,000 times.
- An ecommerce application that accurately predicts prices of unseen products.
- Transition from inference to training, fine-tuning both Frontier and Open-Source models.
- Deploy AI products to production with polished user interfaces and advanced capabilities.
- Level up your AI and LLM engineering skills to be at the forefront of the industry.
- Project 1: AI-powered brochure generator that scrapes and navigates company websites intelligently.
- Project 2: Multi-modal customer support agent for an airline with UI and function-calling.
- Project 3: Tool that creates meeting minutes and action items from audio using both open- and closed-source models.
- Project 4: AI that converts Python code to optimized C++, boosting performance by 60,000x!
- Project 5: AI knowledge-worker using RAG to become an expert on all company-related matters.
- Project 6: Capstone Part A – Predict product prices from short descriptions using Frontier models.
- Project 7: Capstone Part B – Fine-tuned open-source model to compete with Frontier in price prediction.
- Project 8: Capstone Part C – Autonomous agent system collaborating with models to spot deals and notify you of special bargains.
- Hands-On Learning: The best way to learn is by doing. You’ll engage in practical exercises, building real-world AI applications that deliver stunning results.
- Cutting-Edge Techniques: Stay ahead of the curve by learning the latest frameworks and techniques, including RAG, QLoRA, and Agents.
- Accessible Content: Designed for learners at all levels. Step-by-step instructions, practical exercises, cheat sheets, and plenty of resources are provided.
- No Advanced Math Required: The course focuses on practical application. No calculus or linear algebra is needed to master LLM engineering.
- Dive into the fundamentals of Transformers.
- Experiment with six leading Frontier Models.
- Build your first business Gen AI product that scrapes the web, makes decisions, and creates formatted sales brochures.
- Explore Frontier APIs and interact with three leading models.
- Develop a customer service chatbot with a sharp UI that can interact with text, images, audio, and utilize tools or agents.
- Discover the world of Open-Source models using HuggingFace.
- Tackle 10 common Gen AI use cases, from translation to image generation.
- Build a product to generate meeting minutes and action items from recordings.
- Understand the differences between LLMs and how to select the best one for your business tasks.
- Use LLMs to generate code and build a product that translates code from Python to C++, achieving performance improvements of over 60,000 times.
- Master RAG to improve the accuracy of your solutions.
- Become proficient with vector embeddings and explore vectors in popular open-source vector datastores.
- Build a full business solution similar to real products on the market today.
- Move from inference to training.
- Fine-tune a Frontier model to solve a real business problem.
- Build your own specialized model, marking a significant milestone in your AI journey.
- Dive into advanced training techniques like QLoRA fine-tuning.
- Train an open-source model to outperform Frontier models for specific tasks.
- Tackle challenging projects that push your skills to the next level.
- Deploy your commercial product to production with a polished UI.
- Enhance capabilities using Agents.
- Deliver your first productionized, agentized, fine-tuned LLM model.
- Celebrate your mastery of AI and LLM engineering, ready for a new phase in your career.
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Is This Course Worth It?
Expert review by Andrew Derek, Lead Course Reviewer at CoursesWyn. Last updated: .
Based on analysis of the curriculum structure, student engagement metrics, and verified rating data, AI Engineer Core Track: LLM Engineering, RAG, QLoRA, Agents is a high-value resource for learners seeking to build skills in Development. Taught by Ed Donner, Ligency on Udemy, the 41h 30m course provides a structured progression from foundational concepts to advanced Large Language Models (LLM) techniques — making it suitable for learners at all levels. The current coupon reduces the price by 90%, from $109.99 to $10.99, removing the primary financial barrier to enrollment.
What We Like (Pros)
The following advantages were identified:
- Verified 90% price reduction makes this course accessible on any budget.
- Aggregate student rating of 4.7 out of 5 indicates high satisfaction.
- Includes an official Udemy completion certificate and lifetime access.
Keep in Mind (Cons)
The following limitations should be considered:
- The depth of Large Language Models (LLM) coverage may be challenging for newcomers.
- Lifetime access is contingent on the Udemy platform's operation.
- Hands-on projects require additional time beyond video watch time.
"Given the 90% price reduction and verified 4.7-star rating, AI Engineer Core Track: LLM Engineering, RAG, QLoRA, Agents represents one of the strongest value propositions currently available in Development. Enrollment is recommended while this coupon remains active."
Course Rating Summary
AI Engineer Core Track: LLM Engineering, RAG, QLoRA, Agents holds an aggregate rating of 4.7 out of 5 based on 213,950 student reviews on Udemy. The distribution below shows the approximate percentage of students who gave each star rating.
4.7
213,950 Verified Ratings
* Rating distribution is approximated from the aggregate score. Sourced from Udemy. Last verified: .
Instructor Profile
The following section provides background information on Ed Donner, Ligency , the instructor responsible for creating and maintaining AI Engineer Core Track: LLM Engineering, RAG, QLoRA, Agents on Udemy.
AI Engineer Core Track: LLM Engineering, RAG, QLoRA, Agents is taught by Ed Donner, Ligency , a Udemy instructor specializing in Development. For the full instructor biography, professional credentials, and a complete list of their courses, visit the official instructor profile on Udemy.
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Instructor Name: Ed Donner, Ligency
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Subject Area: Development
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Teaching Approach: Practical, project-based instruction focused on real-world application of Large Language Models (LLM) skills.
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Frequently Asked Questions
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Andrew Derek
Expert ReviewerAndrew Derek is a lead editor and course analyst at CoursesWyn with over 8 years of experience in online education and digital marketing. He meticulously audits every Udemy coupon and course syllabus to ensure students get the highest quality learning materials at the best possible price.
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