You should have 1-2 years of experience in software engineering or machine learning, proficiency in Python programming, and a solid understanding of ML fundamentals such as model training and evaluation. Basic knowledge of linear algebra and probability is also helpful for understanding deep learning concepts covered in the first module.

The curriculum spans deep learning fundamentals (neural networks, CNNs, RNNs), transformer and LLM architecture, fine-tuning with PEFT/LoRA, Retrieval Augmented Generation with vector databases, LLM application development using LangChain and LlamaIndex, multimodal applications with Stable Diffusion, production deployment with CI/CD, and advanced topics like constitutional AI and model alignment.

This is an intensive 5-week specialist program designed for working professionals. You should expect to dedicate approximately 15-20 hours per week, including video lessons, hands-on coding exercises, project work, and self-study. The program is structured so that each week builds on the previous one, so consistent engagement is essential.

You will build three industry-relevant projects: a RAG-powered document processing system using vector databases and LangChain, a fine-tuned conversational AI chatbot leveraging PEFT and LoRA adapters, and a multimodal content generation platform combining LLM text generation with Stable Diffusion image generation. Each project involves production-level considerations like deployment, monitoring, and optimization.

This program prepares you for high-demand roles such as Generative AI Engineer, NLP Specialist, Conversational AI Developer, and AI/ML Research Scientist. You'll gain hands-on expertise in the most sought-after GenAI tools and techniques, including LLM fine-tuning, RAG systems, and production deployment—skills that are critical as organizations rapidly adopt generative AI across industries.

You will work extensively with TensorFlow, Keras, Hugging Face Transformers, LangChain, LlamaIndex, Stable Diffusion, and vector databases including Pinecone and Weaviate. You'll also gain experience with quantization methods, PEFT/LoRA adapters, prompt engineering frameworks, and production deployment tools for CI/CD, monitoring, and model versioning.

The Micro Degree course is an online LIVE course, where LIVE sessions will be conducted online on our Classroom platform. Prior to the start of the course, you'll receive preparatory material in the form of recorded content which can be access on the same platform.

In this course instructors will use English language for teaching.

Upon successful registration, you will receive a confirmation email on your registered email ID. In this email you will receive login details for your newly created account on the Edyoda Classroom platform (https://classroom.edyoda.com). Additionally, you will receive a PDF guide containing step-by-step instructions on how to utilize the platform to access live sessions and learning materials.

Our instructors are the industry experts with a minimum working experience of 10 years with a strong technical and teaching background. They bring industry knowledge and practical expertise to the course.

Yes, the course includes online assignments, quizzes, and a final project to reinforce your learning and assess your proficiency in Generative AI Engineer - Specialist.

Yes, you can interact with instructors and fellow students through discussion forums, live Q&A sessions. We encourage a supportive learning community.

We offer a 100% money-back guarantee to ensure your complete satisfaction. If you're not satisfied, you can request a full refund within 3 days of purchase or before the second session, whichever comes earlier. Simply contact our support team(support@edyoda.com) with your purchase details, such as the order ID or email address, and share your reason for the refund. Requests made after 3 days or after the second session will not be eligible for a refund. There are no hidden charges, you will receive the full amount paid. Refunds are processed within 7–10 business days and credited back to your original payment method.