Introduction to Machine Learning

The course covers machine learning concepts and methods. The following topics are covered in the course: basic statistical concepts supervised and unsupervised learning linear and polynomial regression, logistic regression decision trees Support vector machines unsupervised learning using the k-means clustering algorithm algorithm evaluation using cross-validation and mean square error evaluation metrics such as precision, recall, and F-score algorithm implementation using Python