This course will help students learn basic concepts in Machine Learning with a specific focus on helping them be better data science customers. It will introduce basic definitions, key methodologies, and weave in discussions around key questions they should ask as customers.
Learn How To
Distinguish between when a project might be appropriate for Statistical Analysis versus Machine Learning Methods.Identify if the data science team has followed some of the best practices in model constructionDiscuss with the data science team the methods they used for modeling along with the advantages and disadvantages of each methodWho Should Attend
Business managers who currently work with data science teams or who plan to work with data science teams in the future.Statisticians and data analysts who are interested in an introduction to the basic concepts of data science.Prerequisites
None
SAS Products Covered
SAS Machine Learning
Course Outline
Supervised Machine Learning
Methods including: Regression modeling, CART modeling, Naïve Bayes Classification, K-nearest neighbors, Support Vector Machines, Neural Networks, Natural Language Processing.Unsupervised Machine LearningClustering, Recommender systemsMethodology considerationsFeature selection and extraction, Model Averaging, Model Stacking, Boosting 