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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.

Instructors:

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Trees, SVM and Unsupervised Learning

This course includes

12 Hours

Of Self-paced video lessons

Intermediate Level

Completion Certificate

awarded on course completion

Free course

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

Support Vector Machines
Neural Networks
Decision Trees
Statistical Learning
Machine Learning
Unsupervised Learning
Data Science
Random Forests
XGBoost
Model Evaluation

This course includes:

2.37 Hours PreRecorded video

3 programming assignments

Access on Mobile, Tablet, Desktop

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

Osita Onyejekwe
Osita Onyejekwe

1,679 Students

5 Courses

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.

Trees, SVM and Unsupervised Learning

This course includes

12 Hours

Of Self-paced video lessons

Intermediate Level

Completion Certificate

awarded on course completion

Free course

Testimonials

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Frequently asked questions

Below are some of the most commonly asked questions about this course. We aim to provide clear and concise answers to help you better understand the course content, structure, and any other relevant information. If you have any additional questions or if your question is not listed here, please don't hesitate to reach out to our support team for further assistance.