Machine Learning

This course covers topics such as testing and error metrics in machine learning, a friendly introduction to machine learning, deep learning, recommendation systems, matrix factorization, linear regression, support vector machines (SVMs), Naive Bayes classifier, and Thompson sampling. The course aims to teach learners about various machine learning concepts and algorithms, including practical applications and approaches. The intended audience for this course includes individuals interested in expanding their knowledge of machine learning and its real-world applications.