Earn a Computational Machine Learning Certificate from Michigan Engineering

Why Earn a Certificate in Machine Learning?

AI and machine learning are increasingly vital skills across industries, and data scientists and engineers are leading the charge. Learning how to implement machine learning is crucial for success in these fields. With this certificate, you’ll be equipped to design, train, and deploy machine learning systems—skills that will set you apart in the job market.

Machine Learning in Your Career

If you’re a scientist or engineer working with complex data or systems, this course is designed for you. Having the skills to develop and deploy ML models enhances your ability to solve technical challenges. Professionals in many fields, including physics, engineering, and computational science, incorporate machine learning techniques into their work. This allows them to automate processes, analyze large datasets, and uncover new insights. 

By mastering machine learning and deep learning concepts, you’ll be able to apply cutting-edge solutions in your field. This skill set allows you to optimize systems, design experiments, or improve workflows. The knowledge you gain will position you as a leader in adopting AI and machine learning within your organization.

What Will You Learn?

This course requires a technical background and is ideal for professionals with programming, data analysis, or algorithm development experience. The interactive curriculum provides “hands-on experience” building and programming machine learning algorithms from scratch. By working with real-world datasets, you learn to implement machine learning systems and understand their strengths and limitations. 

The course goes in depth in a lot of areas, including:

• Algorithmic and Mathematical Foundations: Learn the basics of machine learning techniques, starting with binary and multi-class supervised classification, and advancing through decision theory.
• Deep Learning: Gain a strong understanding of machine learning deep learning concepts like feedforward networks, convolutional neural nets, and adversarial machine learning.
• Real-World Case Studies: Apply what you’ve learned in practical scenarios, including fruit classification and rock-paper-scissors classification.

By the end of the course, you’ll be able to deploy machine learning applications and evaluate their effectiveness in real-world contexts.

Meet the Instructor

The course is led by Raj Rao Nadakuditi, PhD, a respected professor at the University of Michigan College of Engineering. Dr. Nadakuditi is an expert in AI and machine learning and brings a wealth of experience to the program. He’s dedicated to making this complex subject accessible to learners, ensuring you have the support needed to succeed.

While this course is online and self-paced, the instructional team is there to help. Dr. Nadakuditi and his team are committed to providing a rich, engaging learning experience. They are available to answer questions and help you through any challenges.

How to Get Started

Data scientists, engineers, and technical professionals who want to expand their skill sets in machine learning can apply at any time. You must complete a required pre-test before registration to ensure you’re ready for the technical material. Once enrolled, you’ll have 13 weeks to complete the course, with about 5-6 hours of coursework per week. 

The program also uses an interactive computational textbook called Pathbird, which links mathematical concepts directly to coding exercises. This unique format helps reinforce your learning and ensures you gain practical programming skills in machine learning techniques.

When Can You Enroll?

The course is open for registration now. After completing your application, you will receive registration information and join the next cohort. Once your cohort starts, you can work through the material at your own pace while still having access to instructor support.

Explore this certificate program from Michigan Engineering and take your first step toward mastering AI and machine learning.