9 Months, Online
6-8 hours per week
USD 3,300
Flexible payment options available
Non refundable app fee - USD 50
Special group enrollment pricing
Undergraduate knowledge of statistics, calculus, linear algebra, and probability.
10-question eligibility test on statistics, calculus, linear algebra, and probability to assess participant readiness.
Intermediate knowledge of Python is required as all assignments in the diploma will be done using Python. A complimentary Python for Data Analytics certificate course is available to meet this prerequisite as needed.
Artificial intelligence (AI) and machine learning algorithms are transforming systems, experiences, processes, and entire industries. It’s no wonder that business leaders see these data-driven technologies as fundamental for the future—and that practitioners fluent in both fields are in high demand.
We are fascinated by their world-changing potential, and we’ve created the Postgraduate Diploma in Machine Learning and Artificial Intelligence, to help students understand the fundamentals of AI and machine learning and how to apply them to solve complex, real-world problems.
Supervised Learning
Unsupervised Learning
John has a PhD from Duke and has been a postdoctoral researcher in the Computer Science departments at....
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Ansaf received her PhD in Computer Science from the University of Orleans, France....
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Carleton Smith is a Data Science educator and practitioner in Chicago, IL. Carleton has taught several data science courses at General...
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Jacob is a mathematics educator, with a PhD in mathematics education from Columbia University. Currently, he teaches...
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In addition to Course Leaders, industry experts focusing on data science share their knowledge and experience through periodic guest lectures.
Upon successful completion of the diploma, participants will receive a verified digital diploma from Emeritus Institute of Management.
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John has a PhD from Duke and has been a postdoctoral researcher in the Computer Science departments at Princeton University and UC Berkeley. John Paisley’s research focuses on developing models for large-scale text and image processing applications. He is particularly interested in Bayesian models and posterior inference techniques that address the big data problem.
Ansaf received her PhD in Computer Science from the University of Orleans, France. She was an Associate Research Scientist at the Columbia University’s Center for Computational Learning Systems and served as an adjunct professor with the Computer Science department and the Data Science Institute. Ansaf’s research interests lie in machine learning and artificial intelligence. She has done research on frequent patterns mining, rule learning, and action recommendation and has worked on projects including geographic information systems and machine learning for the power grid. Her current research interest includes crowd sourcing, medical informatics and education.
Carleton Smith is a Data Science educator and practitioner in Chicago, IL. Carleton has taught several data science courses at General Assembly and has served as the Lead Data Science Instructor for a number of corporate training engagements. Before becoming an instructor, Carleton worked on data projects in the finance, health care, and medical research domains. Carleton has an MS in Predictive Analytics from DePaul University and a BS in Entrepreneurship from Indiana University’s Kelley School of Business.
Jacob is a mathematics educator, with a PhD in mathematics education from Columbia University. Currently, he teaches mathematics and computing in the Department of Natural Sciences and Mathematics at The New School in Manhattan, New York. He loves all kinds of problems in mathematics and computing, but especially those dealing with Natural Language Processing and pedagogical problems in the mathematics classroom.