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Machine Learning Foundations and Frameworks

The big data revolution is underway. Are you ready to embrace the machine learning innovations that can lead your company to the top?

Machine Learning Foundations and Frameworks provides essential grounding in the tools and techniques that comprise the field of machine learning. You will discuss the benefits and limitations of machine learning when compared to traditional statistics; illustrate supervised, unsupervised and reinforcement learning; develop research plans for classification and regression; interpret research results from machine learning; and recommend deep learning methods for intelligent systems.

Knowledge Areas

Supervised, unsupervised and reinforced learning • Data transformation • Sampling and resampling • Linear regression • Classification research • Cluster analysis • Neural networks • Image processing • Natural language processing

The Details

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100% Online

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No Mandatory Login Times

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8 Modules

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4-6 Hours of Work per Module

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Course Details

This introductory machine learning course offers an initial background in key tools and skills within the burgeoning field of data science. Learn the fundamentals of data preparation, modeling, analysis and transformation through practice with real-world examples and data sets, and explore the basics of next-generation techniques like deep learning, artificial intelligence and natural language processing.

The Machine Learning Foundations and Frameworks course covers the following topics over eight instructional modules:

  • Module 1: Introducing Machine Learning
  • Module 2: Working With Data and Text
  • Module 3: Building Trustworthy Models
  • Module 4: Regression Models
  • Module 5: Classification Models
  • Module 6: Unsupervised Learning
  • Module 7: Deep Learning
  • Module 8: Machine Intelligence
Thomas Miller

Your Subject-Matter Expert:

Thomas W. Miller, Ph.D.

Thomas W. Miller is faculty director of the data science program at Northwestern University. He designed distance learning training materials for the program, including courses in advanced modeling techniques, marketing analytics, data engineering and machine learning. During the 2019-20 academic year, he will be teaching artificial intelligence and deep learning, natural language processing, and knowledge engineering. Dr. Miller has published six books about data science with Pearson Education (the series "Modeling Techniques in Predictive Analytics"). He also owns a publishing and consulting firm, Research Publishers LLC, located in Manhattan Beach, California. He provides data science consulting services and is actively involved in developing chatbot and survey research applications. Earlier in his career, Dr. Miller worked as a network engineer for NCR Comten and as a field engineer and account representative for Hewlett-Packard Company. He also directed the A. C. Nielsen Center for Marketing Research and taught market research and business strategy at the University of Wisconsin-Madison.

Dr. Miller holds a doctorate in psychology and a master's degree in statistics from the University of Minnesota, as well as an MBA and a master's degree in economics from the University of Oregon.