Specialization

STARTS ON SUNDAY, FEBRUARY 2nd, 2025!

  The Advanced LLM Bootcamp is a 4-month intensive program designed for AI professionals to master Prompt Engineering and Retrieval-Augmented Generation (RAG). Combining theory with hands-on practice, it includes lectures, guided labs, quizzes, real-world projects, research paper readings, and discussion groups. Participants benefit from a supportive faculty, including teaching assistants and a coordinator, with 1-on-1 guidance as needed. The bootcamp offers flexibility to attend in person, remotely, or a mix of both, with live-streamed and recorded sessions ensuring seamless access to all learning materials.

STARTS SOON ON SUNDAY, FEBRUARY 2nd, 2025!

  In the ever-evolving field of artificial intelligence, mastering the nuances of prompt engineering is essential for professionals aiming to harness the full potential of generative AI. This 2-month course on “Advanced Techniques in Prompt Engineering” is meticulously designed for engineers who are keen to deepen their expertise and apply advanced prompt engineering techniques in an enterprise setting.

STARTS SOON ON MONDAY, MAR 31, 2025!

  Elevate your fine-tuning expertise with our immersive hands-on course designed for AI practitioners. Begin with the foundational concepts of transfer learning and pre-trained models, then dive into fine-tuning methodologies for transformers and other state-of-the-art architectures. Explore open-source libraries such as Hugging Face, LoRA, and PEFT for scalable and efficient fine-tuning. Master techniques like prompt tuning, adapter tuning, and hyperparameter optimization to tailor models for domain-specific tasks. Learn strategies for low-resource fine-tuning, including few-shot and zero-shot learning, and address overfitting with advanced regularization methods. Discover fine-tuning approaches for diverse modalities, including text, images, and multimodal data, while exploring domain-adaptation strategies for out-of-distribution datasets. Implement advanced training strategies like quantization-aware training, curriculum learning, and differential privacy. By the end of the course, you’ll have the practical knowledge to fine-tune models for real-world applications, ensuring optimal performance and efficiency tailored to your unique datasets.