Unsupervised Algorithms in Machine Learning
Master essential unsupervised learning techniques including dimensionality reduction, clustering, and matrix factorization for real-world data analysis.
Master essential unsupervised learning techniques including dimensionality reduction, clustering, and matrix factorization for real-world data analysis.
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 Machine Learning: Theory and Hands-on Practice with Python 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.
3.7
(11 ratings)
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What you'll learn
Master Principal Component Analysis for dimensionality reduction
Implement clustering algorithms for pattern discovery
Build recommender systems using collaborative filtering
Apply matrix factorization techniques to real-world problems
Develop practical skills through hands-on Python projects
Skills you'll gain
This course includes:
2.45 Hours PreRecorded video
6 quizzes
Access on Mobile, Tablet, Desktop
FullTime access
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There are 4 modules in this course
This comprehensive course explores fundamental unsupervised learning methods for discovering hidden patterns in unlabeled data. Students learn key techniques including Principal Component Analysis (PCA) for dimensionality reduction, clustering algorithms for pattern discovery, and matrix factorization methods. The curriculum covers practical applications like recommender systems and text classification, with hands-on projects using Python. Topics include similarity metrics, collaborative filtering, singular value decomposition, and non-negative matrix factorization.
Unsupervised Learning Intro
Module 1 · 8 Hours to complete
Clustering
Module 2 · 7 Hours to complete
Recommender System
Module 3 · 7 Hours to complete
Matrix Factorization
Module 4 · 13 Hours to complete
Fee Structure
Instructor
Adjunct Professor
Dr. Geena Kim is an Adjunct Professor in the Computer Science Department at the University of Colorado Boulder, where she specializes in deep learning and machine learning. She holds a Ph.D. from UC Berkeley and has extensive experience in both academia and industry, currently serving as a Research Scientist at Amazon. Her career also includes entrepreneurial ventures and technical advisory roles for Internet of Things (IoT) startups in the Bay Area, showcasing her versatile expertise in cutting-edge technology.Dr. Kim teaches several courses that focus on machine learning techniques, including "Introduction to Deep Learning," "Introduction to Machine Learning: Supervised Learning," and "Unsupervised Algorithms in Machine Learning." Her research interests encompass deep learning, computer vision, and medical image analysis, contributing to advancements in these fields through innovative applications. With a strong commitment to education and research, Geena Kim continues to influence the next generation of computer scientists and data analysts at CU Boulder.
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3.7 course rating
11 ratings
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