{"id":5930,"date":"2025-04-13T19:19:47","date_gmt":"2025-04-13T23:19:47","guid":{"rendered":"https:\/\/engineering.jhu.edu\/ExecEd\/?post_type=course&p=5930"},"modified":"2025-08-07T10:35:00","modified_gmt":"2025-08-07T14:35:00","slug":"jhu-online-course-advanced-methods-in-machine-learning-applications","status":"publish","type":"course","link":"https:\/\/engineering.jhu.edu\/ExecEd\/course\/jhu-online-course-advanced-methods-in-machine-learning-applications\/","title":{"rendered":"Advanced Methods in Machine Learning Applications"},"content":{"rendered":"\n
\n\n
\n

Advanced Methods in Machine Learning Applications<\/h1>\n\n\n
<\/div>\n\n\n\n

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

Make good models great. Model with confidence for real-world results. <\/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\n\n<\/div>\n<\/div>\n\n\n\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 Johns Hopkins faculty<\/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 course with no risk.<\/strong><\/span><\/p>\n<\/div>\n\n<\/div><\/div>\n\n<\/div>\n\n

\n\n
\n\n
\n

Even skilled machine learning practitioners run into models that seem effective on paper but falter in practice\u2014overfitting, lacking transparency, and offering limited real-world value.

If you have solid ML foundations\u2014training classifiers, building regressors, and working with labeled data\u2014you’re ready to level-up.

Dr. Erhan Guven’s<\/a> second course in the Certificate in Theoretical Foundations of Machine Learning<\/a><\/a> introduces the powerful strategies that take your machine learning skills from functional to truly effective:<\/strong> ensemble learning, advanced regression, unsupervised pattern discovery, and reinforcement learning.

You’ll explore how to make models not just better, but more robust, interpretable, and adaptable\u2014the kinds of models that perform in real-world conditions, not just clean lab settings.<\/strong>

Most machine learning models don\u2019t fail because they\u2019re wrong\u2014they fail because they\u2019re fragile, opaque, or built on shaky assumptions. Dr. Guven\u2019s Jupyter notebooks, videos, readings and real-world scenarios <\/strong>will teach you how to build models that last: ones that generalize better, reveal what they’re thinking, and can adapt to changing inputs.

You\u2019ll learn how to work smarter with the models you train\u2014and how to choose the right approach when the data doesn\u2019t follow the rules.

With scikit-learn, pandas, NumPy, and supporting tools like Graphviz and scipy, you\u2019ll experiment with feature selection, dimensionality reduction, and model interpretability to make your systems smarter, faster, and more resilient.<\/strong>

By the end of the course, you\u2019ll be equipped to tackle complex, high-dimensional problems with confidence\u2014and ready to apply machine learning in environments where accuracy, scale, and adaptability matter most.<\/p>\n<\/div>\n\n\n\n

\n

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

This course assumes a working knowledge of Python and prior experience with foundational machine learning concepts. If you\u2019ve previously completed projects involving classification, regression, or data preprocessing with libraries like scikit-learn and pandas, you’re well-prepared for this course. Familiarity with Jupyter notebooks, NumPy, and essential model evaluation techniques will enable you to fully leverage the advanced methods presented here<\/p>\n<\/div>\n\n\n\n

\n

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

Feel confident in your learning journey! If the course 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<\/strong><\/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. 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

Projects You’ll Build (With Expert Guidance)<\/strong><\/h2>\n\n\n\n

With ready-to-use Jupyter notebooks and working code examples, Dr. Guven will walk you through creating…<\/p>\n\n\n\n