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Non-linear Dimensionality Reduction Techniques

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Overview

Unravel the complexities of non-linear dimensionality reduction by mastering t-SNE, geared towards unveiling hidden patterns in multifaceted datasets.

Syllabus

  • Unit 1: Exploring t-SNE for Dimensionality Reduction in Machine Learning
    • Visualizing the Iris Dataset with t-SNE
    • Explore the 3D Space with t-SNE
    • Implementing t-SNE Visualization on Iris Dataset
  • Unit 2: Mastering t-SNE Parameter Tuning in Scikit-learn
    • Visualizing Clusters with t-SNE
    • Exploring the Perplexity of t-SNE
    • Tuning the Stars: Adjusting t-SNE Parameters
    • Space Voyage: Apply and Visualize t-SNE on the Digits Dataset
  • Unit 3: Exploring Locally Linear Embedding: A Dimensionality Reduction Technique
    • Unfolding the Swiss Roll with LLE
    • Adjusting the Number of Neighbors in LLE
    • Squish the Cosmic Data: Tuning LLE Parameters
  • Unit 4: Understanding and Implementing Kernel PCA with sklearn
    • Kernel PCA: Visualizing Transformed Data and Calculating Reconstruction Error
    • Exploring Kernel Functions in Kernel PCA
    • Kernel PCA: Uncover the Hidden Patterns

Reviews

5.0 rating, based on 1 Class Central review

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  • Profile image for Marta Reinhardt
    Marta Reinhardt @martareinhardt
    Conteúdo super bem estruturado, do básico ao avançado, sem pular etapas importantes — ideal tanto pra quem está começando quanto pra quem já tem alguma base e quer aprofundar.
    As aulas são práticas do começo ao fim: muitos projetos reais, exercícios hands-on e códigos comentados que você pode usar no portfólio logo de cara.
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