By Kyle Clark, Enterprise Content Expert
Our new course list for the month of May includes over 30 courses in key topics including computer vision, continuous integration, and learning transfer, which is particularly relevant for Learning and Development professionals.
Here are our top 5 courses for May:
- Data Modeling and Regression Analysis in Business, University of Illinois at Urbana-Champaign – The course will explore data description, statistical inference, and regression. We will extend these concepts to other statistical methods used for prediction when the response variable is categorical. Then you will learn about tools used for identifying important features in the dataset that can either reduce the complexity or help identify important features of the data or further help explain behavior.
- Introduction to Learning Transfer and Life Long Learning (3L), University of California, Irvine – There are many criteria against which the success of training and development activities can be judged. One of the most important, however, is learning transfer. Ultimately, the success of any given training and/or development program is reflected in whether or not what is learned is applied on the job.
- Visual Recognition & Understanding, The State University of New York – This course immerses learners in deep learning, preparing them to solve computer vision problems. Learners plunge into the field of computer vision that deals with recognizing, identifying and understanding visual information from visual data, whether the information is from a single image or video sequence.
- Continuous Integration, University of California, Davis – In today’s world, software development is highly complex and often has large teams of developers working on small pieces of a larger software project. This course will go over the basic principles of using a continuous integration system effectively to constantly improve software.
- Machine Learning Using SAS Viya, SAS – This course covers the theoretical foundation for different techniques associated with supervised machine learning models. A series of demonstrations and exercises is used to reinforce all the concepts and the analytical approach to solving business problems.