Data Analytics: Machine Learning in R

One of the most common jobs performed by data scientists/analysts is prediction using machine learning algorithms. In this course, students will learn the basic concepts of machine learning including training/test sets, overfitting and error rates. The course will also introduce a range of machine learning methods including logistic regression, classification trees, Naive Bayes, random forests and clustering. With the help of several practical applications using R, students will learn to predict the belongingness of an object among various classes.

Fundamentals of Machine Learning [4]

            Supervised Versus Unsupervised Learning

            Regression Versus Classification Problems

            Assessing Model Accuracy

Classification [6]

            The Bayes Classifier

K-Nearest Neighbor

            Logistic Regression

            Linear Discriminant Analysis

Decision Trees [5]

            Bagging

Random Forests

Unsupervised Learning [5]

            Principal Component Analysis

            K-Means Clustering