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Course

Machine Learning

Signal your interest
Course code
CEBD 1260
Duration
30 hours
Get ready to delve into the core concepts and implementation of Machine Learning. What does that mean exactly? Well, based on practical examples from well-known systems like Netflix, YouTube or Twitter, your instructor will describe some fascinating Big Data problems, introduce the standard algorithms used to address them, and present libraries used to implement those algorithms. You'll cover topics like mining of frequent item sets, clustering, stream analysis, similarity search, machine learning and recommendation systems.

Knowledge in Excel, SQL, Python and/or R and statistics and probability are crucial for you to be successful in this course. If you do not have knowledge in these areas, we strongly recommend that you take Intro to data Analysis with Excel (CEBD 1300), Intro to SQL (CEWP 215), Intro to R (CEBD 1200) and/or Intro to Python (CEBD 1100).

Note that you will be required to do 5-10 hours of work per week outside of class time. Those with little to no prior knowledge will require more time to gain familiarity with the concepts.

Upcoming date(s)

There are no upcoming dates at this time.

Your takeaways

This course is a great way to help you:
• Understand the broad classes of methods used to address big data challenges;
• Learn the main algorithms to implement those methods;
• Apply those algorithms on realistic problems using existing software libraries.

Our approach

This course employs student-centered learning that's focused on the acquisition of practical, real-world skills and not just theory. You'll get close, personalized instruction from an industry pro. This course is designed for the students to meet regularly during live synchronous learning in an online virtual classroom (Zoom).

Who benefits the most?

• Students who completed CEBD 1160 and want to continue their studies.
• Individuals who want to improve the performance of their organization by better harnessing Big Data.
• Professionals who want to leverage data for better decision-making.
• Entrepreneurs with projects that could benefit from data analytics.
• Students in fields like geography, biology, psychology, humanities or any other field with big data.
• IT professionals who want to transition to Big Data from more traditional sectors.
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