University of Colorado Boulder - Trees, SVM and Unsupervised Learning
- Offered byCoursera
Trees, SVM and Unsupervised Learning at Coursera Overview
Trees, SVM and Unsupervised Learning
at Coursera
Duration | 12 hours |
Total fee | Free |
Mode of learning | Online |
Official Website | Explore Free Course  |
Credential | Certificate |
Trees, SVM and Unsupervised Learning at Coursera Highlights
Trees, SVM and Unsupervised Learning
at Coursera
- Flexible deadlines Reset deadlines in accordance to your schedule.
- Shareable Certificate Earn a Certificate upon completion
- 100% online Start instantly and learn at your own schedule.
- Coursera Labs Includes hands on learning projects. Learn more about Coursera Labs External Link
- Course 3 of 3 in the Statistical Learning for Data Science Specialization
- Intermediate Level Completion of Regression and Classification and Resampling, Selection & Splines, course part of Statistical Learning for Data Science specialization.
- Approx. 12 hours to complete
- English Subtitles: English
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Trees, SVM and Unsupervised Learning at Coursera Course details
Trees, SVM and Unsupervised Learning
at Coursera
Skills you will learn
More about this course
- "Trees, SVM and Unsupervised Learning" is designed to provide working professionals with a solid foundation in support vector machines, neural networks, decision trees, and XG boost. Through in-depth instruction and practical hands-on experience, you will learn how to build powerful predictive models using these techniques and understand the advantages and disadvantages of each. The course will also cover how and when to apply them to different scenarios, including binary classification and K > 2 classes. Additionally, you will gain valuable experience in generating data representations through PCA and clustering. With a focus on practical, real-world applications, this course is a valuable asset for anyone looking to upskill or move into the field of data science.
- This course can be taken for academic credit as part of CU Boulder's Master of Science in Data Science (MS-DS) degree offered on the Coursera platform. The MS-DS is an interdisciplinary degree that brings together faculty from CU Boulder's departments of Applied Mathematics, Computer Science, Information Science, and others. With performance-based admissions and no application process, the MS-DS is ideal for individuals with a broad range of undergraduate education and/or professional experience in computer science, information science, mathematics, and statistics. Learn more about the MS-DS program at https://www.coursera.org/degrees/master-of-science-data-science-boulder.
Trees, SVM and Unsupervised Learning at Coursera Curriculum
Trees, SVM and Unsupervised Learning
at Coursera
Welcome!
Course 3 Introduction
Welcome and Where to Find Help
Support Vector Machines (SVMs)
Support Vector Machines: Part 1
Support Vector Machines: Part 2
Support Vector Machines: Part 3
Support Vector Machines: Part 4
Support Vector Machines
Introduction to Neural Networks
Neural Networks: Part 1
Neural Networks: Part 2
Neural Networks: Part 3
Neural Networks: Part 4
Neural Networks And Its Application To Unsupervised Learning
Neural Networks
Decision Trees-Bagging-Random Forests
Decision Trees
Decision Trees and Bagging
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