Advanced Methods in Machine Learning Applications
Coursera
18 Courses
Johns Hopkins University is a globally recognized research university comprising 9 schools and campuses worldwide. It provides more than 260 degree programs, ranging from undergraduate to graduate and postdoctoral studies.
Overview
The course "Advanced Methods in Machine Learning Applications" delves into sophisticated machine learning techniques, offering learners an in-depth understanding of ensemble learning, regression analysis, unsupervised learning, and reinforcement learning.
Emphasizing practical application, this course teaches students how to apply advanced techniques to solve complex problems and optimize model performance. Learners will explore methods like bagging, boosting, and stacking, as well as advanced regression approaches and clustering algorithms.
What sets this course apart is its focus on real-world challenges, providing hands-on experience with advanced machine learning tools and techniques. From exploring reinforcement learning for decision-making to applying apriori analysis for association rule mining, this course equips learners with the skills to handle increasingly complex datasets and tasks.
By the end of the course, learners will be able to implement, optimize, and evaluate sophisticated machine learning models, making them well-prepared to address advanced challenges in both research and industry.
University: Johns Hopkins University
Provider: Coursera
Categories: Machine Learning Courses, Reinforcement Learning Courses, Unsupervised Learning Courses, Regression Analysis Courses, Ensemble Learning Courses, Decision Trees Courses
