This new course, by Columbia University Adjunct Professor, Asha Saxena, extends our eLearningCurve CIMP in Data Science curriculum. 

Machine learning is an application of artificial intelligence (AI) that provides systems with the ability to automatically learn and improve from experience without being explicitly programmed. It is pervasive today in everyday life from recommendation engines to practical speech recognition, web searches to advanced GPS systems. Businesses are taking advantage of machine learning in creating advanced solutions to serve their customer segments.

Just as humans learn by example, machine learning algorithms learn by example. Machine learning allows us to both learn from the past to inform the future and give our data a voice. There are two equally important components for the successful application of machine learning: a good algorithm, and a comprehensive set of training examples that span as much of the system-of-interest parameter space as possible.

A broad introduction to Machine Learning

This course provides students with a broad introduction to machine learning and the data preparation workflow.

The course begins with a portfolio of case studies to provide an overview of what can be accomplished with machine learning. Then, the fundamental machine learning tasks and algorithms are covered. The machine learning tasks and algorithms covered include multivariate nonlinear nonparametric regression, supervised classification, unsupervised classification, and deep learning. For these machine learning tasks, it is shown how to assess the quality of the machine learning models and perform error estimation and feature engineering.

This course is geared towards data engineers, data analysts and other data management professionals interested in learning how to apply machine learning in business applications. The course will help you to understand best practices for applying machine learning.




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