Non-linear Dimensionality Reduction Techniques
Unravel the complexities of non-linear dimensionality reduction by mastering t-SNE, geared towards unveiling hidden patterns in multifaceted datasets.
Lessons and practices
Lesson 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
Lesson 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
Lesson 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
Lesson 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
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