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Building Recommender systems with Gaussian Mixture Model

IntermediateGuided Project

Building Recommender systems, creating anomaly detection algorithm or performing customer segmentation are all very complicated but yet common tasks. Gaussian Mixture Model is a powerful probabilistic algorithm that can be a great tool to perform all of those tasks and more. In this guided project, you will learn how to identify complex patterns, clusters, and subgroups in your datasets by using GMMs.

4.6 (81 Reviews)

Language

  • English

Topic

  • Data Science

Enrollment Count

  • 384

Offered By

  • IBMSkillsNetwork

Estimated Effort

  • 30 minutes

Platform

  • SkillsNetwork

Last Update

  • May 11, 2025
About this Guided Project
GMM is a versatile unsupervised learning technique that finds applications in various domains, such as image segmentation, anomaly detection, customer behavior analysis and so on. Throughout this project, you will gain a comprehensive understanding of GMM's underlying concepts and practical implementation techniques, equipping you with valuable skills for extracting meaningful insights from your data.



Who should participate?

This guided project is ideal for data scientists, machine learning practitioners, and enthusiasts eager to unlock the potential of probabilistic clustering. Participants should have a basic understanding of Python programming fundamentals. No prior experience with Gaussian Mixture Model is required, as we will cover the necessary theory and practical implementations.

Instructors

Joseph Santarcangelo

Senior Data Scientist at IBM

Joseph has a Ph.D. in Electrical Engineering, his research focused on using machine learning, signal processing, and computer vision to determine how videos impact human cognition. Joseph has been working for IBM since he completed his PhD.

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Roxanne Li

Data Scientist at IBM

I am an aspiring Data Scientist at IBM with extensive theoretical/academic, research, and work experience in different areas of Machine Learning, including Classification, Clustering, Computer Vision, NLP, and Generative AI. I've exploited Machine Learning to build data products for the P&C insurance industry in the past. I also recently became an instructor of the Unsupervised Machine Learning course by IBM on Coursera!

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Artem Arutyunov

Data Scientist

Hey, Artem here! I am excited about answering new challenges with data science, machine learning and especially Reinforcement Learning. Love helping people to learn, and learn myself. Studying Math and Stats at University of Toronto, hit me up if you are from there as well.

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