0390 - Unsupervised Machine Learning

Course Description

Overview 

Unsupervised machine learning is a powerful tool for extracting meaningful information from unlabelled data. Unlike supervised learning, where the algorithm is provided with labelled examples to learn from, unsupervised learning enables you to find patterns, relationships, and structures within data without explicit guidance. It also helps address a wider variety of challenges, contributing to more thorough analysis of complex data sets.

This course covers the key concepts and practices of unsupervised machine learning, providing you with hands-on experience using Python for real-world, unsupervised learning tasks and applications. You will learn the most popular unsupervised learning techniques and discover how they can be used to discover patterns and similarities in unlabelled data sets without human intervention.

Upon successful completion of the course, you will have an enhanced machine learning portfolio to show potential employers and be equipped with the skills needed to apply common unsupervised learning techniques in practical ways.

Who Should Enroll 

• Current or aspiring data analysts or data scientists looking to build a machine learning portfolio.
• Data professionals looking to add machine learning techniques to their domain.
• Individuals with basic knowledge in programming and mathematics who want to expand their machine learning knowledge and skills.

What you will learn 

• Understand and explain the principles and significance of unsupervised machine learning.
• Reduce the number of input variables or features in large data sets using dimensionality reduction techniques.
• Analyze data for patterns or co-occurrences using association rule techniques.
• Discover hidden patterns or clusters within unlabelled data sets using clustering techniques.

Course Details 

• Expert instruction from University of Waterloo faculty.
• Learn at your own pace with weekly independent online learning and hands-on exercises and come away with a machine learning portfolio that demonstrates your skills.
• Optional drop-in sessions twice weekly via Zoom where you can ask questions and receive instructor support on key course concepts:
  • Wednesdays, 2 - 2:30 p.m. ET
  • Wednesdays, 6 - 6:30 p.m. ET
• Access to online discussion boards.
• Approximately five hours of your time each week.

Applies Towards the Following Certificates

• Machine Learning Practitioner : Mandatory
• Machine Learning Project Specialist : Mandatory