MSOE’s Graduate Certificate in Applied Machine Learning was created out of industry demand. Step up as a leader with machine learning skills and knowledge employers need.

In-Depth. 
Industry-Driven. Professionally Executed.

By clicking "Request a Program Brochure,” I agree to provide the contact information listed above for the purpose of receiving communications regarding educational programs and opportunities.

© Milwaukee School of Engineering

  • Collect and prepare data for analysis

  • Create visualizations and perform statistical analyses to support data-driven decision making

  • Communicate results and actionable insights to non-technical audiences

  • Analyze technical problems to identify appropriate modeling techniques and develop solutions that meet requirements from resource-constrained embedded systems up to large-scale web services with millions of users

This program prepares you to:

MACHINE LEARNING SKILLS ARE IN DEMAND

MSOE’s Graduate Certificate in Applied Machine Learning program builds on your existing software development and engineering skills and your valuable professional experience. This 9-month program provides an accelerated, in-depth introduction to machine learning concepts and theory using practical, hands-on exercises. Learn directly from our expert, experienced faculty. 

Hiring for machine learning skills is up 417% from 2015.

Employers need professionals with machine learning and artificial intelligence (AI) experience to process data, make predictions, inform critical decisions, and lead adoption of new technologies.1

  • Develop and evaluate new predictive models using cutting-edge approaches such as deep learning

  • Learn problem-solving strategies and techniques for improving model prediction performance

  • Learn how to apply machine learning to diverse data types such as spreadsheets, images, text, sensor readings and more

No Waiting. Apply What You Learn to Where You Work.

MSOE’s Graduate Certificate in Applied Machine Learning program delivers hard-to-hire-for skills.

Program highlights

Go Deep on Machine Learning and AI

Learn to apply machine learning and AI technologies with two highly-focused courses. In AI Tools and Paradigms, you’ll learn the tools, languages, and methods used in modern AI practice. In Applied Machine Learning, you will explore the study and construction of algorithms that can learn from and make predictions on data.

Focus on domains of your choosing to apply the skills you learn in class to your current professional work. Examples of projects include visualization and analysis of geospatial distributions of real estate, engineering new features to improve machine learning model prediction performance, object detection and segmentation with deep neural networks, and AI agents for video games.

Project-Based Learning

Get the high-value, hard-to-hire-for skills employers need. MSOE has a reputation among employers for cultivating effective practitioners who leave MSOE’s programs with immediately applicable skills.

Employers Know and Trust MSOE

Learn directly from our faculty members—no TA intermediaries here. MSOE faculty are experts in machine learning and have worked for companies like Red Hat, AdRoll and NVIDIA. Get direct feedback through synchronous courses and flexible office hours.

Access to the Experts

GROW YOUR KNOWLEDGE. STRENGTHEN YOUR CAREER.

Request a Program Brochure

Drive Your Career Forward With a Graduate Certificate in Applied Machine Learning From Milwaukee School of Engineering 

DURATION
9 months, 8 credit hours

SMALL CLASS SIZES
Build close connections to classmates and faculty

FORMAT
100% online, synchronous evening courses

— Max M., Graduate Certificate in Applied Machine Learning ’22

The depth of analytical knowledge I gained from the program gives me the context for continued independent learning. Now, I know how to ask the right questions.”

  • Collect and prepare data for analysis

  • Create visualizations and perform statistical analyses to support data-driven decision making

  • Communicate results and actionable insights to non-technical audiences

  • Analyze technical problems to identify appropriate modeling techniques and develop solutions that meet requirements from resource-constrained embedded systems up to large-scale web services with millions of users

  • Develop and evaluate new predictive models using the cutting-edge approaches such as deep learning

  • Learn problem-solving strategies and techniques for improving model prediction performance

  • Learn how to apply machine learning to diverse data types such as spreadsheets, images, text, sensor readings, and more

Request a Program Brochure