Machine Learning Primer
Daniel D. Gutierrez, AMULET Analytics
Machine learning can be thought of as a set of tools and methods that attempt to infer patterns and extract insight from enterprise data assets. The subject of machine learning is one that has matured considerably over the past several years. Machine learning has grown to be the facilitator of the field of data science, which is, in turn, the facilitator of big data. In this session, I will provide a high-level overview of the field by examining the two primary types of statistical learning: supervised learning and unsupervised learning. Supervised learning is the most common type, often associated with predictive analytics. We’ll discuss two classes of supervised algorithms to make predictions: regression and classification. Next, we’ll discuss the most common type of unsupervised algorithm: clustering to discover previously unknown patterns within the data.
About the Speaker
Daniel D. Gutierrez is a practicing data scientist through his Santa Monica, Calif. consulting firm AMULET Analytics. Daniel also serves as Managing Editor for insideBIGDATA.com where he keeps a pulse on this dynamic industry. He is also an educator and teaches classes in data science, machine learning and R for universities and large enterprises. Daniel holds a BS degree in mathematics and computer science from UCLA.