The subject matter of this undergraduate-level introductory course is to provide students a broad overview of many concepts and algorithms in Machine Learning (ML) and equip students with the skills to apply these concepts to real world problems. After completing the course, the students, learn about the basic concepts in main machine learning. Topics will include Nearest Neighbor Classifier, Linear Regression, Least Squares, Learning Theory, Statistical Estimation: MLE, MAP, Naive Bayes Classifier, Linear Classification Models: Logistic Regression, Linear Discriminant Functions, Support Vector Machines, Decision Tree Learning, Ensemble Methods: Bagging, Boosting, Clustering, Ethics in Machine Learning, Feature Engineering: Extraction and Selection.