AI Professional Certificate

Graduate level

Some subject area knowledge is required.

Gain a solid and structured foundation in Artificial Intelligence through this comprehensive professional certificate. Designed to equip you with both theoretical insight and practical understanding, this program includes four university-level courses.

Module 1: Introduction to AI
Module 2: Machine Learning
Module 3: Deep Learning Fundamentals
Module 4: Natural Language Processing

• Module 1: The Introduction to Artificial Intelligence course is designed to help learners decode the mystery of Artificial Intelligence and understand its business applications.

The course provides an overview of Artificial Intelligence concepts and workflows, Machine Learning, Deep Learning, and performance metrics.
Students learn the difference between supervised, unsupervised, and reinforcement learning; are exposed to use cases; and see how clustering and classification algorithms
help identify Artificial Intelligence business applications.

• Module 2: This Machine Learning course examines the automation of data analysis to enable computers to learn and adapt through experience to do specific tasks without explicit programming. Students will master Machine Learning concepts and techniques, including supervised and unsupervised learning, mathematical and heuristic aspects, and hands-on modeling to develop algorithms and prepare students for their role with advanced Machine Learning knowledge.
• Module 3: This module is designed to help students learn the fundamentals of Deep Learning. It will make students familiar with the concepts of Deep Learning, Convolutional neural networks, and the effectiveness of Deep Learning. This course examines the rapidly growing field of Data Science with neural networks.
• Module 4: This Natural Language Processing course will give Students a detailed look at the science

behind applying Machine Learning algorithms to process large amounts of natural language data. Students will learn the concepts of Natural Language understanding, Feature Engineering, Natural Language Generation and Speech Recognition techniques.

The online delivery blends synchronous and asynchronous components. Students complete self-directed assignments hosted on the course platform. Weekly Forums support a required active, rubric-based student contributions that foster collaboration.

Exams may include essays, short answers, or MCQs. Formats include open/closed book. Time zones are considered. Assessments are formative and summative. Students have 2 weeks to review grades and must follow the appeal process in the Student Handbook.

Certificate issued on the Blockchain

Provided by the instructor at the beginning of the course.