Machine Learning Using R
This course focuses on using the language R for machine learning, which is concerned with algorithms that transform information into actionable intelligence.
What you can learn.
- 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. The machine learning language, R, is a cross-platform, zero-cost statistical programming environment, which offers a powerful but easy-to-learn set of tools that can assist students with finding data insights. 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.
It is advisable that you complete the following (or equivalent) since they are prerequisites for Machine Learning Using R.
Fall 2018 Schedule
These courses are fully online, and there are no in-person classroom meetings.
Enrollment limited. Enrollment deadline: October 1, 2018. Internet access required. Materials required.
Hybrid courses utilize a combination of in-person classroom meetings and online meetings.
Enrollment limited. Enrollment deadline: October 3, 2018. Internet access required. Materials required.