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An Introduction to Machine Learning at Data Summit 2018


From recommendation systems to disease diagnosis, machine learning is revolutionizing the process of complex decision making by enabling the analysis of bigger, more complex datasets and the delivery of faster, more accurate results.

During the first day of Data Summit, May, 21, 2018, Marina Johnson, assistant professor of analytics, information management/business analytics, Montclair State University and Drexel University, held a pre-conference workshop examining how to develop scalable, real-world machine learning pipelines and applications.

“Many algorithms we use today date back to the 17th-18th century,” Johnson said.

Because storage is cheaper than it’s ever been, businesses can run algorithms and new fields have emerged through this progress. Abundant scientific programming languages have emerged and it makes it easier to use data science.

Industries using data science with machine learning has expanded to include descriptive analytics, predictive analytics, and prescriptive analytics, she explained.

Logistic Regression is a machine learning algorithm designed to predict categorical variables. This type of prediction is called classification. Linear Regression predicts outcomes between – and + infinity values in graphs.

The goal is to find an equation to represent the dependent variable using a linear combination of coefficients and independent variables.

Decision trees are used for classification problems but they can also be used for regression problems as well, Johnson said.

“A decision tree is a great algorithm for multiple datasets,” Johnson said.

Attendees of the workshop learned about the different algorithms behind successful machine learning to glean insight from data.

Data Summit 2018 is taking place at the Hyatt Regency Boston, May 22-23, with pre-conference workshops on Monday, May 21. Cognitive Computing Summit will also be co-located at the event.

For more information about Data Summit 2018, and to register, go here.


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