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Machine Learning: Algorithms & Applications
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Module 11
Unsupervised Learning: Dimensionality Reduction
1
Understanding the Curse of Dimensionality
Explain the 'curse of dimensionality' and the importance of dimensionality reduction for machine learning.
2
Understanding Principal Component Analysis (PCA)
Explain Principal Component Analysis (PCA), including its goal of variance maximization and the concept of principal components.
3
PCA for Dimensionality Reduction and Explained Variance
Apply PCA to a high-dimensional dataset and choose the number of components based on explained variance.
4
PCA for Supervised Learning: Impact on Performance and Training Time
Use PCA as a preprocessing step for a supervised learning model and evaluate its impact on performance and training time.
t-SNE vs. PCA: Visualizing High-Dimensional Data
Explain t-SNE as a non-linear dimensionality reduction technique for data visualization and contrast its purpose with PCA.
Previous module
Unsupervised Learning: Clustering
Next module
Model Selection and Final Project