This comprehensive course introduces participants to the end-to-end process of developing AI tutors tailored to specific knowledge domains. Designed for both beginners and experienced developers, it offers a structured learning path to create intelligent, domain-specific AI systems. Throughout the course, students will learn to design, implement, and optimize AI tutors using cutting-edge tools like Python, Hugging Face, TensorFlow, and Llama, gaining hands-on experience with real-world applications.
The course begins with foundational concepts, including data preprocessing and extracting insights from domain-specific datasets. Students will explore techniques for generating knowledge representations using methods like TF-IDF and embeddings, setting the stage for building effective question-answering (QA) systems. Advanced modules cover the integration of pre-trained models like BERT and GPT, as well as leveraging Llama for extractive summarization, enabling AI tutors to provide precise and context-aware responses.
Participants will also delve into critical aspects of deployment, including creating scalable, reliable, and secure AI systems using cloud and local infrastructures. Emphasis is placed on monitoring and continuously improving AI tutors through feedback, retraining, and performance optimization.
With practical coding exercises, step-by-step guidance, and insights into real-world use cases in education, healthcare, and law, this course equips students with the skills to transform learning experiences through AI. By the end, participants will be ready to deploy their own customized AI tutors for specific domains.
