Machine Learning Foundations and Frameworks

Data Science Discipline:
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.

Length
8 weeks
Cost
$1,500
Next Start
09/21/2020

Installment payments are now available on all course purchases!

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
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8 Modules

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4-6 Hours of Work per Module

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Thomas Miller, Ph.D.

Your Subject Matter Expert:

Thomas Miller, Ph.D.


“To be a data scientist, you have to be multilingual: You have to speak the language of business, the language of statistics and the language of information technology. Think of data science as the discipline and machine learning as a technology or group of technologies within that discipline.”

Course Modules

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.

Module 1: Introducing Machine Learning

Describe the role of the data scientist. Explain how probability is a measure of uncertainty. Discuss benefits and limitations of traditional statistics versus machine learning. Contrast supervised, unsupervised and reinforced learning.

Module 2: Working with Data and Text

Contrast levels of measurement. Recommend methods of data transformation. Organize data for modeling. Recommend methods for converting text to numbers.

Module 3: Building Trustworthy Models

Justify methods of data selection. Design sampling and resampling plans. Recommend methods for correcting bias and variance. Interpret results from model training and testing.

Module 4: Regression Models

Identify variable roles in models. Illustrate traditional linear regression. Design machine learning models for regression. Evaluate results from regression analysis.

Module 5: Classification Models

Illustrate traditional classification research. Design machine learning models for classification. Interpret classification research results. Design research for addressing issues with classification models.

Module 6: Unsupervised Learning

Illustrate methods for measuring distance between objects. Contrast partitioning and hierarchical methods of cluster analysis. Describe methods of dimension reduction. Design machine learning methods for autoencoders.

Module 7: Deep Learning

Define deep learning. Describe alternative neural network designs. Illustrate convolutional neural networks and recurrent neural networks.

Module 8: Machine Intelligence

Contrast logic programming and machine learning approaches to artificial intelligence. Design research for image processing. Design research for natural language processing. Evaluate methods for machine intelligence.

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Thomas 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.