This course covers data preparation for machine learning. The course includes example applications and example code. Best practices in telecommunication, data cleaning, data transformation, and how to turn the developed algorithms into a robust system are covered in depth. The benefits for taking this course include turning your users and system administrators into heroes, having a system that can remain viable, and successfully moving into the next great application while handing the system to lower level people.
What am I going to get from this course?
Select the appropriate robot for their application, integrate the real time data exchange at the appropriate frequency, include the predictors that may be needed for training, prepare the data for machine learning, and deploy robustly for success.
Prerequisites and Target Audience
What will students need to know or do before starting this course?
A knowledge of Python, C++, Kafka, et al, will help if the student really wants to be able to deploy, but these sections can be skipped for the functional user or executive who is interested at the survey level because the class is in the "for dummies" style.
Who should take this course? Who should not?
Because the course is in the "for dummies" style, anyone from functional staff to executives can take the course at the survey level. To really get to implement these topics, because the underlying math is not covered, a masters in quantitative methods is advised.