AI Engineer Bootcamp 2026: LLMs, RAG, AI Agents & Vector DBs (Udemy.com)

Content and topics are good but voice could be better. Also the choice of slang-like words slightly put off. The trailing sound towards the end of sentences and husky sound at other places are really put me off. In general trainings require clear voice. Without headphones (and sometimes with), I had to concentrate so hard to get what word was being said.

I retired from the Computer Industry as a programmer and instructor before AI became commercial. My experimentation with ChatGPT made me more curious of this world. I took a Udemy course titled, "Ultimate ChatGPT for Beginners" by Joshua George and my eyes were opened. However, I wanted more as a developer, so now I'm here. Your intro intrigued me. I'd like to know of the infrastructure of AI.

The course was really good, I was a JavaScript guy and don't have any knowledge of the AI and python that much but this course really helped me to get the entire basics and Give me the picture of real AI world, Though some modules are little old, I think the videos are recorded may be in the start of 2025 yet for a complete beginner it was wonderful

I think this AI Engineer course is one of the best beginner-friendly courses because it covers all the important top-level concepts in a very easy and understandable way. Thank you for this wonderful opportunity, @infosys, for collaborating with Udemy and making quality learning accessible to everyone.

Excelente curso, toca practicamente todos los temas importantes para dar un panorama bastante detallado sobre IA, LLMs y Agentes, tanto de forma teorica como practica. Algo largo, pero vale la pena para formar una buena base.

Great course covering AI/LLM engineering from fundamentals to advanced topics. The instructor explains core concepts well and provides useful deep dives into how things work and how to apply them in real scenarios. However, the hands-on sections felt overwhelming at times. Large blocks of code were often copied and pasted, and the explanations weren’t always clear enough to fully understand the implementation. Overall, a very informative and comprehensive course, especially for learners serious about mastering AI and LLMs.

Great course for gaining a solid overview of current GenAI and LLM frameworks and concepts. This is a hands-on, coding-focused course—ideal for programmers and developers looking to integrate LLMs into their applications. Don’t expect deep dives into the mathematical foundations or inner workings of Transformers; the emphasis here is on practical implementation rather than theory.

While the course covers important concepts, I found there was an overemphasis on theoretical content that would have been more effectively demonstrated through practical, hands-on tutorials. The explanations tended to be repetitive at times, which unnecessarily extended the video durations. Additionally, the slide presentations could benefit from better organization and visual clarity to enhance the learning experience. Despite these areas for improvement, I appreciate the effort put into developing this course material.

Overall, I'd rate this AI course on Udemy 4.5 out of 5 stars. It offers significant value for the price and provides a solid introduction to the field. The course does an excellent job of introducing a wide range of concepts related to Artificial Intelligence (AI) and Large Language Models (LLMs). The practical exercises are diverse and well-chosen, effectively illustrating the theoretical concepts covered in the lectures. It successfully provides learners with foundational knowledge and concrete hands-on examples to understand how to work with both free and paid AI models. However, it's crucial to understand the prerequisites for this course. It is best suited for learners who already have a foundational understanding of programming, are familiar with APIs, and especially have experience with Python. The instructor's teaching style involves pasting blocks of code and explaining them rather than building code line-by-line from scratch. This approach requires students to actively engage and work to understand the code's functionality themselves. If you're a complete beginner to programming or Python, you might find this challenging. A notable area for improvement is the final section covering Fine Tuning and LoRA techniques for model training. This part feels quite introductory and is taught relatively quickly. It introduces many new elements within the code snippets without deep explanation. If your primary goal for taking this course is to gain in-depth practical knowledge of fine-tuning techniques, I would caution against it. The instructor provides a conceptual overview but does not delve deeply into the code implementation for these advanced topics. Another minor drawback is the instructor's limited interaction and response rate to student questions. This can be frustrating if you encounter issues or need clarification during the course. Despite these points, I found the course to be a valuable investment. I acquired a substantial amount of new knowledge without spending a lot. The instructor consistently provided motivation and offered advice for further exploration, which was very helpful in maintaining momentum and encouraging continued learning throughout the course. In summary, if you have a programming background (especially in Python) and are looking for a broad introduction to AI and LLMs with practical examples, this course is an excellent choice and offers great value. Just be aware of the style of coding instruction and the limited depth on advanced topics like fine-tuning

Learning about LLM models and RAGs was a fantastic experience. The teacher, Paulo Dichone, provided clear, engaging explanations of complex concepts, making the material both accessible and interesting. His use of Python to demonstrate practical applications really helped solidify my understanding. The course is highly relevant today, especially with the rise of AI technologies. I appreciated how the lessons on PyLlama and Langchain were integrated into the curriculum, giving me a hands-on feel for working with these tools in real projects. I would give this class an A+ because it not only taught me valuable knowledge but also made me think critically about how LLMs can be applied in practical scenarios. Plus, the community aspect was a big plus—getting to collaborate and discuss ideas with like-minded individuals really deepened my learning experience. Overall, I feel confident that this course will prepare me well for more advanced studies in AI and machine learning. If there were any challenges or areas where I could have improved further, it would have been great to explore those after completing the course!