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This course intended to teach the basics of machine learning to the students, who have no technical background mainly students of management and social sciences. The course cover basics of supervised and unsupervised machine learning approaches. After completion of this course, the students will be able to understand the working principal of machine learning and decision making based on the machine learning model outputs. The student will also be learning to apply the machine learning approaches in various problem solving. The core learnings of this course will be the regression problems and classification types of problem under supervised machine learning. The specific algorithm, such as linear regression, logistic regression, K-nearest neighbor and decision tree will be demonstrated, for solving such problems. Under unsupervised machine learning, the association problem and clustering problems will be taught. Under unsupervised learning, association rule mining and k-means clustering will be demonstrated. The entire course will of 120 minutes of lectures and demonstration. The algorithm will be demonstrated in MS Excel and python. This hands-on experience of implementing the various machine learning algorithms in various problem solving will make the course more interesting and exciting.
