Building Recommender systems with Gaussian Mixture Model

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.

Guided Project

Data Science

309 Enrolled

4.6

(63 Reviews)

At a Glance

Name: Building Recommender systems with Gaussian Mixture Model
Price: Free CAD
Rating: 4.6 (63 reviews)
Author: joseph_santarcangelo, roxanne_li, artem_arutyunov

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.

Offered By: IBMSkillsNetwork

Building Recommender systems with Gaussian Mixture Model

At a Glance

Who should participate?

This Guided Project is part of the following Learning Paths