Unsupervised Machine Learning
Master clustering algorithms and dimensionality reduction techniques for advanced data analysis.Unsupervised Machine Learning
Master clustering algorithms and dimensionality reduction techniques for advanced data analysis.Unsupervised Machine Learning
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 IBM Machine Learning Professional Certificate 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.
4.7
(253 ratings)
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English
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What you'll learn
Master various clustering algorithms and their applications
Implement dimensionality reduction techniques
Understand distance metrics and their impact
Apply matrix factorization methods
Evaluate and compare clustering algorithms
Develop practical solutions for real-world problems
Skills you'll gain
This course includes:
4 Hours PreRecorded video
14 assignments
Access on Mobile, Tablet, Desktop
FullTime access
Shareable certificate
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There are 7 modules in this course
This comprehensive course covers advanced unsupervised learning techniques for data analysis. Students learn various clustering algorithms including K-means, DBSCAN, and hierarchical clustering, along with dimensionality reduction methods like PCA and matrix factorization. The curriculum emphasizes practical implementation through hands-on labs while exploring theoretical concepts such as the curse of dimensionality and distance metrics.
Introduction to Unsupervised Learning and K Means
Module 1 · 3 Hours to complete
Distance Metrics & Computational Hurdles
Module 2 · 3 Hours to complete
Selecting a Clustering Algorithm
Module 3 · 4 Hours to complete
Dimensionality Reduction
Module 4 · 4 Hours to complete
Nonlinear and Distance-Based Dimensionality Reduction
Module 5 · 2 Hours to complete
Matrix Factorization
Module 6 · 3 Hours to complete
Final Project
Module 7 · 1 Hours to complete
Fee Structure
Instructors
Pioneering Data Scientist Bridging AI Research and Education
Dr. Joseph Santarcangelo, a Data Scientist at IBM, brings a unique blend of academic excellence and practical expertise to the field of data science and artificial intelligence. With a Ph.D. in Electrical Engineering, his groundbreaking research focused on the intersection of machine learning, signal processing, and computer vision to understand how video content influences human cognitive processes. At IBM, he has established himself as a prominent educator and course developer, creating comprehensive learning materials that have reached hundreds of thousands of students worldwide. His teaching portfolio encompasses a wide range of technical subjects, from foundational Python programming to advanced topics in artificial intelligence, machine learning, and computer vision. Santarcangelo's ability to translate complex technical concepts into accessible learning experiences has made him an influential figure in data science education, maintaining consistently high ratings from learners while continuing to push the boundaries of applied machine learning and artificial intelligence research.
Digital Content Delivery Lead at IBM with Extensive Experience in Information Technology Education
Mark J. Grover is a Digital Content Delivery Lead at IBM, specializing in the creation and delivery of online educational content. Before joining IBM, he was a full-time professor of computer technology at Cape Fear Community College in Wilmington, NC, where he coordinated the Information Security program and taught various courses including Computer Security and Network Administration. Grover has over 25 years of experience in information technology and has received accolades such as the Cisco Instructor of Excellence award and the Award for Excellence in Innovation from the University of North Carolina Wilmington. He is passionate about outdoor activities like camping and mountain biking, and enjoys spending time with his family.
Testimonials
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4.7 course rating
253 ratings
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