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(If you are paying by Check or Zelle upfront, then you get the discounted rate of $3200)

STARTS  ON THURSDAY, Jan 29, 2026!

  This 12-week curriculum equips AI engineers with theoretical knowledge and practical skills in Retrieval-Augmented Generation (RAG). Through hands-on projects and programming sessions, participants gain expertise in implementing and optimizing RAG systems for real-world applications, building a strong project portfolio and confidence to tackle advanced AI Search.

STARTS SOON — DATE TBD!

  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.

(If you are paying by Check or Zelle upfront, then you get the discounted rate of $3200)

STARTS WEDNESDAY, JAN 28, 2026!

  This course is designed to introduce students to the foundational and advanced concepts of artificial intelligence, with a focus on neural networks, large language models, and generative AI. Through a combination of lectures, hands-on coding exercises and project work, students will gain a deep understanding of the mathematical and technical underpinnings of AI technologies. They will explore the theory behind neural networks, delve into various architectures, and understand the applications and implications of AI in the real world.