Following up on the fundamentals started in Introduction to Data Science, this course explores all the basic parts of executing a machine learning experiment. Upon successful completion of this course, you will be able to design your own machine learning experiments.
Machine Learning I will present basic algorithms, such as regression, C4.5 decision trees, and Nearest Neighbours, with an emphasis on the fundamentals of properly designing a machine learning experiment which includes cross validation and evaluation metrics. At the end of this course you will be able to create your own ML pipeline, describe the differences between classic ML algorithms and of their best fit use cases, and build well performing ML models.
Lessons are taught with the scikit-learn library in python. It is recommended that you complete CCTB391 Introduction to Data Science prior to enrolling in this course.