In this course, students explore core Natural Language Processing (NLP) techniques and methods to train, refine, and apply language models effectively in enterprise settings. Participants will engage with practical exercises covering tokenization, sentiment analysis, text summarization, and transformer based language models. The course emphasizes real world scenarios, providing learners with practical experience and skills to implement NLP driven solutions that address actual business problems, improve operational efficiency, and enhance decision making processes.

Participants learn practical strategies to create clear, effective, and concise prompts that optimize interaction between users and AI systems. This course covers principles of effective prompt design, including how prompts influence AI behavior, accuracy, and user experience. Students examine case studies illustrating successful prompt engineering and common challenges faced in prompt formulation. By the end of this course, learners will have acquired the ability to craft prompts tailored to specific enterprise tasks, enhancing the reliability and usability of AI solutions in real world applications.

This practical course introduces students to essential data analytics methods commonly utilized in enterprise AI applications, including Natural Language Processing (NLP), Named Entity Recognition (NER), which identifies and categorizes key information like names, places, and dates from unstructured text, Retrieval Augmented Generation (RAG), which improves AI responses by retrieving relevant external information, and NLP to SQL techniques, which convert natural language queries into database commands. Students gain hands on experience applying these analytics approaches directly to organizational data, addressing realistic business challenges and enhancing data driven decision making.

In this course, learners explore the principles and methodologies involved in designing and deploying AI agents to automate tasks within complex enterprise environments. The course covers various AI agent frameworks, including LanGraph and LangChain, teaching students how to structure, manage, and orchestrate these agents effectively to handle tasks such as data extraction, routine automation, and decision support. Students engage in realistic scenarios and project based activities to develop practical skills in building and deploying reliable and efficient AI agents tailored for organizational needs.

This specialized course provides detailed knowledge of Human Digital Twins (HDTs), which are virtual replicas specifically modeling human behaviors or manual processes within enterprise operations. Students learn how HDTs replicate and simulate human actions to improve process efficiency, consistency, and decision accuracy in workflows like customer support, administrative operations, and manual data entry processes. Through hands on projects and realistic simulations, learners develop practical skills to create, implement, and manage HDTs, enabling precise forecasting, performance monitoring, and improved organizational management.

The capstone practicum integrates all acquired knowledge, providing students an opportunity to develop and implement comprehensive enterprise AI projects. Learners collaboratively design AI driven workflows, construct practical AI solutions, and apply learned methods to authentic business scenarios. The course emphasizes real world problem solving, effective teamwork, and clear communication of results. Upon completion, students will have demonstrated their capability to deliver tangible, industry relevant AI solutions, ensuring readiness for professional roles in enterprise AI implementation.