Learn how to collect data and explore and prepare the data for the machine learning algorithm
Learn how to select the appropriate machine learning algorithm for the data and proposed task
Learn how to train a model, and evaluate and improve the model performance
About this course:
This course focuses on machine learning, which is concerned with algorithms that transform information into actionable intelligence. This field is made possible due to the rapid and simultaneous evolution of available data, statistical methods, and computing power. Students learn the origins and practical applications of machine learning, how knowledge is defined and represented by computers, and the basic concepts that differentiate machine learning approaches. Machine learning algorithms can be divided into two main groups: supervised learners that are used to construct predictive models and unsupervised learners that are used to build descriptive models. Students learn the classification, numeric predictor, pattern detection, and clustering algorithms. Students learn to train a model, evaluate its performance, and improve its performance. Algorithm uses are illustrated with real-world cases, such as breast cancer diagnosis, spam filtering, identifying bank loan risk, predicting medical expenses, estimating wine quality, identifying groceries frequently purchased together, and finding teen market segments.
COM SCI X 450.1 Introduction to Data Science, or consent of instructor.