t-SNE | Visualizing High Dimension Data Hands-on | Neighbor Embedding | Unsupervised Learning

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In this comprehensive tutorial, we introduce advanced data visualization using t-SNE (t-distributed Stochastic Neighbor Embedding). We provide a step-by-step demonstration using Python to visualize the output of various clustering techniques, including k-means, hierarchical, k-medoids, Gaussian Mixture Models (GMM), and DBSCAN.
🔥 Key Takeaways:
Learn the fundamentals of t-SNE and how it works.
Discover how to implement this technique in Python using popular libraries.
Dive into real-world like examples to see t-SNE in action.
Comparison with PCA
👨‍💻 Hands-On Demonstration:
We'll walk you through creating both 2D and 3D visualizations using Plotly Express, a powerful data visualization library in Python. Witness how clusters emerge and patterns unfold in a visually intuitive manner.
🚀 Who Is This Tutorial For?
This tutorial is perfect for data enthusiasts, machine learning practitioners, and anyone interested in exploring advanced data visualization techniques. You'll gain valuable insights and hands-on experience in this tutorial.
📊 What You'll Accomplish:
Master the art of visualizing high-dimensional data effectively.
Gain a deeper understanding of clustering techniques and their outputs.
Learn how to create stunning interactive visualizations using Plotly Express.