371 - Advanced Dimensionality Reduction: t-SNE vs UMAP vs PCA Deep Dive скачать в хорошем качестве

371 - Advanced Dimensionality Reduction: t-SNE vs UMAP vs PCA Deep Dive

PCA not cutting it for complex data visualization? Discover the power of non-linear dimensionality reduction! Learn when linear methods fail and how t-SNE and UMAP reveal hidden patterns in high-dimensional data. What You'll Learn: Why non-linear methods outperform PCA for visualization Complete t-SNE implementation and theory: Probability distributions and neighbor embedding Perplexity parameter tuning (5-50 range) When t-SNE creates the best cluster separation Advanced UMAP techniques: Topological data analysis fundamentals n_neighbors vs min_dist parameter effects Why UMAP preserves both local AND global structure Case Study: Breast Cancer Classification Using 569 tumor samples with 30 features - but these methods revolutionize ANY complex data: customer segmentation, genomics, image analysis, text mining, social networks, and more. Complete Python Implementation: Parameter tuning with visual comparisons Clustering performance metrics (silhouette scores) Side-by-side method comparisons Runtime and scalability analysis Publication-quality visualizations Part of the Statistical Analysis in Python Tutorial Series Code from the video: https://github.com/bnsreenu/python_fo...