Advanced Methods in Machine Learning Applications
Master sophisticated machine learning techniques including ensemble methods, advanced regression, unsupervised learning, and reinforcement learning algorithms.
Master sophisticated machine learning techniques including ensemble methods, advanced regression, unsupervised learning, and reinforcement learning algorithms.
This course cannot be purchased separately - to access the complete learning experience, graded assignments, and earn certificates, you'll need to enroll in the full Applied Machine Learning Specialization program. You can audit this specific course for free to explore the content, which includes access to course materials and lectures. This allows you to learn at your own pace without any financial commitment.
Instructors:
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What you'll learn
Implement and optimize ensemble learning methods
Apply advanced regression techniques for complex data
Master unsupervised learning algorithms
Develop reinforcement learning solutions
Perform apriori analysis and pattern mining
Skills you'll gain
This course includes:
9.5 Hours PreRecorded video
12 assignments
Access on Mobile, Tablet, Desktop
FullTime access
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There are 5 modules in this course
This comprehensive course explores advanced machine learning techniques, focusing on ensemble methods, regression analysis, unsupervised learning, and reinforcement learning. Students learn to implement sophisticated algorithms including bagging, boosting, clustering, and Q-learning. The curriculum covers practical applications of these techniques to solve complex problems, optimize model performance, and handle advanced data analysis challenges.
Course Introduction
Module 1 · 10 Minutes to complete
Ensemble Learning
Module 2 · 4 Hours to complete
Regression
Module 3 · 4 Hours to complete
Unsupervised Learning
Module 4 · 3 Hours to complete
Reinforcement Learning and Apriori Analysis
Module 5 · 7 Hours to complete
Fee Structure
Instructor
Innovator in Cybersecurity and Machine Learning at Johns Hopkins University
Dr. Erhan Guven is a distinguished faculty member at Johns Hopkins University, where he teaches courses in machine learning, generative AI, natural language processing (NLP), graph analytics, and formal methods. He holds a Ph.D. in Computer Science from The George Washington University and has made significant contributions to the fields of cybersecurity and disease forecasting through his extensive research and numerous publications. Dr. Guven is also an inventor of several patents related to speech emotion detection and Voice over Internet Protocol (VoIP) technologies, showcasing his commitment to advancing technology in practical applications. His interdisciplinary approach combines theoretical knowledge with real-world problem-solving, making him a valuable asset to both the academic community and the broader field of computer science. Through his teaching and research, Dr. Guven continues to inspire students and contribute to cutting-edge advancements in technology.
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