{"id":3978,"date":"2025-04-06T22:12:31","date_gmt":"2025-04-07T02:12:31","guid":{"rendered":"https:\/\/engineering.jhu.edu\/ExecEd\/?post_type=course&p=3978"},"modified":"2025-09-15T12:11:01","modified_gmt":"2025-09-15T16:11:01","slug":"certificate-theoretical-machine-learning","status":"publish","type":"course","link":"https:\/\/engineering.jhu.edu\/ExecEd\/course\/certificate-theoretical-machine-learning\/","title":{"rendered":"Certificate in Theoretical Foundations of Machine Learning"},"content":{"rendered":"\n
\n\n
\n

Certificate in Theoretical Foundations of Machine Learning<\/h1>\n\n\n

Start anytime. Learn at your own pace.<\/p>\n\n\n

Put ML and neural networks to work for your data and career. <\/p>\n\n\n

\n
Start Now<\/a><\/div>\n<\/div>\n\n\n\n
Request Info<\/a>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n\n\n\n
\n
\"\"<\/figure>\n\n
\n\n
\n
\n

<\/rect><\/g><\/path><\/g><\/polygon><\/polygon><\/polygon><\/g><\/svg><\/span>Artificial Intelligence<\/span><\/span><\/p>\n\n\n\n

<\/title><\/path><\/path><\/path><\/circle><\/svg><\/span>Online Self-Paced<\/span><\/span><\/p>\n<\/div>\n\n\n\n
\n\n<\/div>\n<\/div>\n\n\n\n
\n

$1500 <\/p>\n\n\n\n

$1200 <\/p>\n<\/div>\n\n\n\n

Instructor: Dr. Erhan Guven<\/a><\/p>\n\n<\/div><\/div>\n<\/div>\n\n\n

\n\n
\n

<\/path><\/svg><\/span>Curriculum designed and delivered by Hopkins APL’s Dr. Ehran Guven<\/a><\/span><\/p>\n\n\n\n

<\/path><\/svg><\/span>LIVE monthly seminars and office hours<\/a> <\/span><\/p>\n\n\n\n

<\/path><\/svg><\/span>Engaging learning including video walkthroughs and hands-on activities<\/a><\/span><\/p>\n\n\n\n

<\/path><\/svg><\/span>Satisfaction guaranteed<\/a>. Explore the certificate with no risk.<\/strong><\/span><\/p>\n\n\n\n

<\/path><\/svg><\/span>Save $300 with the certificate vs. buying courses separately<\/span><\/p>\n<\/div>\n\n<\/div><\/div>\n\n<\/div>\n\n

\n\n
\n\n
\n

Machine learning is driving breakthroughs\u2014not by replacing expertise, but by enabling insights and capabilities that were once out of reach. To put it to work\u2014in your data, your research, your career\u2014it\u2019s not enough to tweak GitHub code or borrow models that answer someone else’s questions.<\/strong> You need to understand and build them from the ground up.

In his three-course<\/a> Certificate in Theoretical Foundations of Machine Learning, Dr. Erhan Guven<\/a> guides you through the full machine learning workflow<\/strong>: from how to clean and prepare data all the way to building deep learning neural networks.

From his experience on mission-critical projects at the Johns Hopkins Applied Physics Laboratory and teaching hundreds of online master\u2019s students, Dr. Guven designed a program that gets you up and running quickly with applied, real-world applications.<\/strong>

Through Jupyter notebooks with working code examples, video walkthroughs, quizzes, readings, and hands-on projects, you’ll get the conceptual grounding to reason through model design and performance.<\/strong>

Using tools like scikit-learn, pandas and PyTorch, you\u2019ll build models that don\u2019t just \u201crun,\u201d but make meaningful predictions, reveal patterns, and generalize to new data.

With Dr. Guven’s guidance, you’ll design powerful systems that recognize images and understand audio. You’ll get the skills to solve real world problems in business intelligence, research, healthcare, and software development using the same cutting-edge deep learning techniques elite researchers and industry giants are using to push the boundaries of science and technology.<\/p>\n\n\n\n

\n

The Certificate combines Dr. Guven’s 3 sequential ML courses:<\/strong><\/p>\n\n\n\n

<\/div>\n\n\n\n

Into one bundle, saving you $300 off the cost of buying each course separately.<\/strong><\/p>\n<\/div>\n\n\n\n

Earning the Certificate<\/h2>\n\n\n\n

After completing the course content, you can earn your Certificate by submitting a capstone project\u2014proving to colleagues and employers that you’re ready to take the lead in complex machine learning projects that drive insights and ROI.<\/strong><\/p>\n\n\n\n

\n
\n\n<\/div>\n\n\n\n
\n

The capstone requires applying all skills from the 3-course certificate into a single deliverable that demonstrates knowledge of state-of-the-art ML and includes sufficient performance metrics. The project will be reviewed by Dr. Guven, who will provide feedback, which can be discussed further during live office hours. <\/p>\n\n\n\n

The image is for illustrative purposes only. Actual certificate design subject to change,<\/em><\/p>\n<\/div>\n<\/div>\n<\/div>\n\n\n\n

\n

No Risk: Satisfaction Guaranteed<\/h2>\n\n\n\n

Feel confident in your learning journey! If the certificate content is too advanced, not advanced enough<\/em>, or simply doesn\u2019t meet your expectations, we\u2019ve got you covered with our money-back guarantee. Just contact our team within 7 days from purchase to receive a full refund\u2014no questions asked.<\/strong><\/p>\n<\/div>\n\n\n\n

\n
\n

Meet Your Instructor<\/h2>\n\n\n\n

Dr. Erhan Guven<\/h3>\n\n\n\n

Johns Hopkins University, Johns Hopkins Applied Physics Laboratory<\/em><\/p>\n<\/div>\n\n\n\n

\n
\n\n<\/div>\n\n\n\n

Dr. Guven is an AI scientist at Johns Hopkins University Applied Physics Laboratory and assistant program manager in Johns Hopkins Engineering’s #1 ranked online master’s programs in AI and data science. His research spans a broad spectrum of machine learning applications, including large language models, financial systems cybersecurity, NLP, and bioinformatics. He earned his PhD in computer science from George Washington University. In his spare time, he enjoys gardening, beekeeping, building computers, and playing Defense of the Ancients.<\/p>\n<\/div>\n\n\n\n

Dr. Guven is Here to Help!<\/strong><\/h4>\n\n\n\n

Questions about course content? Looking compare model results or get feedback? Stop by monthly Zoom office hours<\/strong> to talk with Erhan and fellow students.<\/p>\n<\/div>\n\n\n\n

\n

Prerequisites <\/h2>\n\n\n\n

You should be familiar with Python programming, including experience writing and running scripts. While extensive coding expertise isn’t required, comfort with foundational programming concepts and tools such as NumPy or pandas will be beneficial. Prior experience with machine learning is not necessary\u2014however, a willingness to engage with mathematical concepts like algebra and basic statistics will help you grasp key techniques. <\/p>\n<\/div>\n\n\n\n

\n

Course Summaries <\/h2>\n\n\n\n
\n
\n
\n1. Techniques & Applications<\/span>\n<\/div>\n\n\n\n
\n2. Advanced Methods<\/span>\n<\/div>\n\n\n\n
\n3. Neural Networks & Model Regularization<\/span>\n<\/div>\n<\/div>\n\n\n\n
\n
\n

Jump into building working classification systems while ensuring you have the essential knowledge of data preparation and feature engineering\u2014the keys to ML success.

Dr. Guven\u2019s Jupyter notebooks will keep you on the right track, helping you understand the machine learning workflow while challenging you to implement key components yourself. You\u2019ll develop practical coding skills (and confidence) as you progress from data preparation through model training to evaluation and refinement.

With scikit-learn, pandas, and real-world datasets you\u2019ll implement your first Support Vector Machines, Naive Bayes classifiers and text categorizing systems. Then you\u2019ll make them more accurate and more predictive.

By the conclusion of this course, you\u2019ll be able to implement the complete machine learning workflow and have an excellent foundation to build upon.

You’ll build…
<\/strong><\/p>\n\n\n\n