In the course Supervised Machine Learning on Udacity, you will learn various concepts and techniques related to regression, perceptron algorithms, decision trees, naive Bayes, support vector machines, ensemble of learners, evaluation metrics, and training and tuning models. You will understand the difference between regression and classification, train linear regression models, and use logistic regression to predict states. You will also learn about perceptron algorithms for classification, decision trees for state prediction, and naive Bayes algorithm for spam message prediction. Additionally, you will explore support vector machines for data separation, ensemble of learners for data visualization, and evaluation metrics for model performance measurement. Finally, you will train and test models using Scikit-learn, choose the best model using evaluation techniques, and apply these concepts in a course project to find donors for CharityML while considering marketing constraints.