This course introduces you to one of the main types of modeling families of supervised Machine Learning: Classification. You will learn how to train predictive models to classify categorical outcomes and how to use error metrics to compare across different models. The hands-on section of this course focuses on using best practices for classification, including train and test splits, and handling data sets with unbalanced classes.
About this Course
IBM is the global leader in business transformation through an open hybrid cloud platform and AI, serving clients in more than 170 countries around the world. Today 47 of the Fortune 50 Companies rely on the IBM Cloud to run their business, and IBM Watson enterprise AI is hard at work in more than 30,000 engagements. IBM is also one of the world’s most vital corporate research organizations, with 28 consecutive years of patent leadership. Above all, guided by principles for trust and transparency and support for a more inclusive society, IBM is committed to being a responsible technology innovator and a force for good in the world.
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TOP REVIEWS FROM SUPERVISED MACHINE LEARNING: CLASSIFICATION
Superb ,detailed, well explained, lots of hands on training through labs and most of the major alogrithms are covered! Keep up the good work. You guys are helping the community a lot :D
I would like to give especial thanks to the instructor (the one in the videos) for his great job. It would be nice to know who is is.
Great course and very well structured. I'm really impressed with the instructor who give thorough walkthrough to the code.
A well-structured and practical course which helps me answer lots of my concerns from the past until now.
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