🎓 Welcome to our hands-on tutorial on Gaussian Mixture Models (GMM)! 📊 In this video, we'll be exploring how GMM can be applied to the college survey dataset to segment colleges into three distinct groups based on various attributes.

Pre-requisite videos:
GMM theory -    • What are Gaussian Mixture Models? | S...  
Dataset preparation -    • Complete Hands-on Tutorial | Cluster ...  
Unsupervised Learning Playlist - https://tinyurl.com/5ea8sesz

🔍 We'll start by preparing our dataset, which includes cleaning, preprocessing, and selecting features to ensure that our data is in the best possible shape for analysis. This step is crucial to ensure that our GMM results are accurate and meaningful.

💻 Next, we'll dive into the implementation of GMM. In our theory video, we have explain the concept of GMM and how it differs from other clustering algorithms, such as K-means. In this video, we'll apply GMM to analyze the results to understand the three segments of colleges that emerge.

📈 Throughout the video, we'll provide clear explanations to help you understand the intricacies of GMM and how it can be applied to real-world datasets. Whether you're new to machine learning or looking to deepen your understanding of clustering algorithms, this tutorial is perfect for you.

Happy Learning!
📝 In the description below, you'll find links to the dataset and code used in this tutorial, allowing you to follow along and try out GMM for yourself. Don't forget to subscribe to our channel for more hands-on tutorials and data science insights! Let's dive in and uncover the hidden patterns in our college survey dataset using GMM!