The McMaster Continuing Education office will be closed on Monday, May 19 for Victoria Day. We will respond to inquiries within 1-2 business days once we re-open on Tuesday, May 20.
Building on the fundamental principles of data analytics, this course advances to modern machine learning techniques such as neural network, deep learning, and reinforcement learning as well as NLP and text analysis. Application activities will be structured to provide an introductory level of how machine learning techniques are applied to big data analytics. Learners should have a strong level of data analytics for this course. DAT 203 Predictive Modelling and Data Mining is recommended prior to registering in this course.
Tuesday, May 6 - July 29: 7:00 p.m. - 10:00 p.m. (Virtual)
Location:
ONLINE
Instructor:
Karim Souidi
Notes:
This course is offered as a virtual classroom with classes held, as per the scheduled day/time. Classes will be held online, using a combination of synchronous, real-time, instructor led sessions, supplemented with asynchronous activities. You should expect to attend class at the same time, each week, although not every session will be a full 3 hours of live instruction. Rather, classes will provide learners the opportunity to apply what has been covered by your course facilitator. Laptop computer required; No class July 1. Please note registering after the course start date may delay course access and does not permit exceptions to coursework due dates, late textbook purchases, or the refund policy
Instructors
Karim Souidi
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Karim is Statistician by training, a Certified Analytics Professional, an active member of the American Statistical Association, and a DASCA certified Senior Data Scientist. He’s also a former Fulbright scholar at U.C. Berkeley. His past teaching experience includes Big Data Analytics courses and Capstone project supervision at Ryerson University and Sheridan College. Karim has also held multiple senior analytics positions in the public service of Canada and Ontario. He’s most passionate about non-linear dimension reduction methods.
