Trees, SVM and Unsupervised Learning
Master statistical learning with decision trees, SVMs, and neural networks. Perfect for data science professionals.
Master statistical learning with decision trees, SVMs, and neural networks. Perfect for data science professionals.
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 Statistical Learning for Data Science 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.
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What you'll learn
Master Support Vector Machines for classification tasks
Implement neural networks and understand their architecture
Apply decision trees and ensemble methods effectively
Analyze strengths and weaknesses of different algorithms
Create powerful predictive models using statistical learning
Skills you'll gain
This course includes:
2.37 Hours PreRecorded video
3 programming assignments
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FullTime access
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There are 4 modules in this course
This comprehensive course focuses on advanced statistical learning methods including Support Vector Machines (SVMs), neural networks, and decision trees. Students learn to implement these powerful algorithms for classification and prediction tasks, understanding their theoretical foundations and practical applications. The curriculum covers kernel functions, backpropagation, ensemble methods like bagging and random forests, and techniques for model evaluation and optimization.
Welcome!
Module 1 · 32 Minutes to complete
Support Vector Machines (SVMs)
Module 2 · 3 Hours to complete
Introduction to Neural Networks
Module 3 · 4 Hours to complete
Decision Trees-Bagging-Random Forests
Module 4 · 4 Hours to complete
Fee Structure
Instructor
Assistant Professor at the University of Colorado Boulder
Dr. Osita Onyejekwe is an Assistant Professor at the University of Colorado Boulder, where he specializes in multivariate regression models and machine learning techniques. His research focuses on estimating weather patterns, analyzing glacier recession behavior, and developing financial models related to profit gains, losses, and revenue. In addition to his quantitative research interests, Dr. Onyejekwe explores topics in planetary systems, abiogenesis, philosophy, and theology, reflecting a diverse academic curiosity that bridges the sciences and humanities. His interdisciplinary approach aims to contribute valuable insights across various fields while enhancing the understanding of complex systems and their interactions.
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Frequently asked questions
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