Professional Certificate in PCA Actionable Knowledge
-- ViewingNowThe Professional Certificate in PCA Actionable Knowledge is a comprehensive course designed to equip learners with essential skills for career advancement in the data analysis industry. This program focuses on Principal Component Analysis (PCA), a crucial technique for data compression and visualization.
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โข PCA Fundamentals: Introduction to Principal Component Analysis, linear algebra review, covariance and correlation matrices, eigenvalues and eigenvectors.
โข Data Preprocessing: Data cleaning, missing value imputation, normalization, scaling, transformation, and dimensionality reduction.
โข Implementing PCA in Python: Using popular data science libraries like NumPy, SciPy, and scikit-learn to implement PCA.
โข Visualizing PCA Results: Plotting principal components, eigenvalues, scree plots, and biplots in 2D and 3D.
โข Use Cases of PCA: Exploring real-world applications of PCA in finance, marketing, computer vision, and natural language processing.
โข PCA Evaluation Metrics: Quantitative evaluation of PCA performance using reconstruction error, explained variance, and silhouette scores.
โข PCA Variants: Comparing classical PCA with Kernel PCA, Non-negative Matrix Factorization (NMF), Independent Component Analysis (ICA), and t-distributed Stochastic Neighbor Embedding (t-SNE).
โข PCA Limitations and Alternatives: Understanding when PCA is not the best choice, and exploring alternatives such as Linear Discriminant Analysis (LDA), Factor Analysis, and Autoencoders.
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