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Trees, SVM and Unsupervised Learning
  • Level Professional
  • Duration 13 hours
  • Course by University of Colorado Boulder
  • Offered by

About

"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.

Modules

Course Introduction

    • 1 Videos
    • 2 Readings
    • 1 Discussion
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1 Discussions

  • Introduce Yourself!

1 Videos

  • Course 3 Introduction

2 Readings

  • Earn Academic Credit for your Work!
  • Course Support

Introduction to SVMs

    • 4 Videos
    • 1 Readings
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4 Videos

  • Support Vector Machines: Part 1
  • Support Vector Machines: Part 2
  • Support Vector Machines: Part 3
  • Support Vector Machines: Part 4

1 Readings

  • Support Vector Machines

Assignments

    • 1 Lab
    • 1 Programming
Show info about module content

1 Programming

  • SVMs Assignment

1 Labs

  • SVMs Practice Lab

Neural Networks

    • 5 Videos
    • 1 Readings
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5 Videos

  • Neural Networks: Part 1
  • Neural Networks: Part 2
  • Neural Networks: Part 3
  • Neural Networks: Part 4
  • Neural Networks And Its Application To Unsupervised Learning ​​

1 Readings

  • Neural Networks

Assignments

    • 1 Programming
Show info about module content

1 Programming

  • Neural Networks Lab and Assignment

Introduction

    • 1 Videos
    • 1 Readings
Show info about module content

1 Videos

  • Decision Trees

1 Readings

  • Decision Trees and Bagging

Assignments

    • 1 Lab
    • 1 Programming
Show info about module content

1 Programming

  • Decision Trees Assignment

1 Labs

  • Decision Trees Walkthrough

Auto Summary

"Trees, SVM, and Unsupervised Learning" is a professional-level course tailored for working professionals seeking expertise in support vector machines, neural networks, decision trees, and XG boost. Offered by Coursera and part of CU Boulder’s Master of Science in Data Science (MS-DS) degree, the course provides practical, real-world applications in data science. It covers binary classification, multi-class scenarios, PCA, and clustering over 780 minutes of content. With no application process, it’s ideal for individuals with varied backgrounds in computer science, information science, mathematics, and statistics. Available through a Starter subscription.

Osita Onyejekwe
Instructor

Osita Onyejekwe