EFFECTIVE METHODS OF MACHINE LEARNING
Program opens on February 3, 2025. Investment: $1,200
3 Courses • 12 Weeks • 60 Hours • Online
• Hands-on Projects • $1,200
4 Weeks • Online • Hands-on Projects
4 Weeks • Online • Hands-on Projects
4 Weeks • Online • Hands-on Projects
Unlock your potential in machine learning with this fully online certificate program from Johns Hopkins University. Designed to equip professionals with practical knowledge and advanced techniques, this program offers three specialized courses to help you master the field of principles and methods of machine learning through hands-on learning and expert guidance.
Program Highlights:
Earn a Johns Hopkins University Certificate of Achievement and gain the knowledge and tools to excel in applied machine learning.
To earn a Johns Hopkins University School of Engineering Certificate of Achievement, you must complete the following:
This program is designed for professionals with prior experience in machine learning who want to enhance their expertise in applying advanced techniques to solve data-driven problems. It is ideal for data scientists looking to deepen their understanding of foundational and advanced machine learning methods. And engineers and developers aiming to integrate machine learning models into practical applications will also benefit from this program
Designed by JHU Faculty, the curriculum covers key skills in machine learning, including:
Enhance your learning experience through monthly live seminars designed to deepen your expertise and allow you to engage directly with faculty experts at the forefront of their fields, gaining valuable insights, practical advice, and real-world perspectives.
Johns Hopkins University faculty are readily accessible to address your questions directly through the course platform, ensuring personalized guidance and a seamless learning experience.
Learn at your own pace with engaging and helpful learning materials such as video lectures, relevant readings, and case studies.
Hands-on activities that help you demonstrate competence and enabling reskilling and upskilling.
Johns Hopkins University Instructor
Dr. Sheppard is a Norm Asbjornson College of Engineering Distinguished Professor in the Gianforte School of Computing at Montana State University and is a former Adjunct Professor in the Computer Science Department at Johns Hopkins. His research interests include model-based and Bayesian reasoning, reinforcement learning, game theory, and fault diagnosis/prognosis of complex systems. He is a Fellow of the IEEE, elected “for contributions to system-level diagnosis and prognosis.”
Dr. Sheppard received his BS in computer science from Southern Methodist University in 1983. Later, while a full-time member of industry, he received an MS in computer science in what is now Johns Hopkins Engineering for Professionals (1990). He continued his studies and received his Ph.D. in computer science from Johns Hopkins in the day school (1997), completing a dissertation on multi-agent reinforcement learning and Markov games.
Prior to entering academia full time, Dr. Sheppard was a member of industry for 20 years. His prior position was as a research fellow at ARINC Incorporated. Dr. Sheppard became a member of the EP faculty in 1994 where he teaches courses in machine learning and population-based algorithms. He also mentors independent studies and advises several graduate students. In 2022, he received the Provost’s Award for Graduate Research and Creativity Mentoring at Montana State University, which recognizes excellence in advising MS and PhD students.
Gain expertise in the principles and methods of machine learning to drive innovation, solve complex challenges, and stay competitive in today’s rapidly evolving tech landscape.
10 Weeks • Online • Hands-on Projects
10 Weeks • Online • Hands-on Projects
10 Weeks • Online • Hands-on Projects
Submit your details below to learn more about the course curriculum, benefits, fee and more.
Submit your details below to learn more about the course curriculum, benefits, fee and more.