Principal Component Analysis Courses and Certifications

Unsupervised Learning and Clustering

Master unsupervised learning algorithms from scratch, including k-Means, PCA, and DBSCAN. Build clustering implementations, visualize results, and evaluate performance using essential metrics.

• CodeSignal
• 3 hours
• Self-Paced
• Free Certificate

Linear Landscapes of Dimensionality Reduction

Master dimensionality reduction techniques through Linear Discriminant Analysis (LDA), comparing it with PCA while implementing practical Python solutions for improved feature selection and model performance.

• CodeSignal
• 2 hours
• Self-Paced
• Free Certificate

Career Certificate

Mastering Dimensionality Reduction with Python

Master Python-based dimensionality reduction techniques from PCA to autoencoders, learning to extract essential features from complex datasets and optimize machine learning model performance through hands-on practice.

• CodeSignal
• 15 hours
• Self-Paced
• Free Certificate
• 5 courses

Feature Selection, Reduction and Streamlining

Master techniques to identify impactful features, reduce dimensionality, and streamline datasets for enhanced model efficiency and performance by focusing on the most relevant data attributes.

• CodeSignal
• 1 hour
• Self-Paced
• Free Certificate

Career Certificate

Mastering Dimensionality Reduction with R

Discover how to simplify complex data using PCA, linear/non-linear techniques, autoencoders, and feature selection in R for enhanced machine learning performance.

• CodeSignal
• 15 hours
• Self-Paced
• Free Certificate
• 5 courses

Linear Landscapes of Dimensionality Reduction

Discover Linear Discriminant Analysis techniques for dimensionality reduction and feature selection through hands-on R programming exercises with real datasets.

• CodeSignal
• 2 hours
• Self-Paced
• Free Certificate

Unsupervised Learning and Clustering

Master unsupervised learning by building k-Means, mini-batch k-Means, PCA, and DBSCAN from scratch. Assess cluster quality using key metrics like homogeneity and completeness.

• CodeSignal
• 3 hours
• Self-Paced
• Free Certificate

Linear Algebra for Data Science: Practical Applications with Python

Jump in this 20 Hours course to master essential linear algebra techniques for turning raw data into actionable insights using Python and to elevate your analytical and computational skills to attain proficiency in high-impact data solutions with real-wo…

• OpenLearning
• 20 hours
• Self-Paced
• Paid Course

Applied Unsupervised Learning in Python

Master unsupervised learning algorithms in Python to uncover hidden patterns, reduce dimensions, cluster data, model topics, and handle missing values using real-world datasets.

• Coursera
• 1 day 6 hours 13 minutes
• Self-Paced
• Paid Course

Genomic Data Science and Clustering (Bioinformatics V)

94 ratings at Coursera

Explore genomic data clustering techniques for gene expression analysis and human migration patterns. Learn algorithms for data point grouping and dimensionality reduction using principal components analysis.

• Coursera
• 9-10 hours
• Self-Paced
• Paid Course

Matrix Methods

249 ratings at Coursera

Explore fundamental matrix methods essential for machine learning and data analysis, including multiplication, linear equations, orthogonality, and SVD for dimensionality reduction and PCA.

• Coursera
• 6 hours 35 minutes
• Self-Paced
• Paid Course

Academic Lesson

SPSS Tutorial for Data Analysis - SPSS for Beginners

7 ratings

Comprehensive SPSS tutorial covering various statistical analyses, from basic tests to advanced regression techniques. Ideal for beginners seeking practical data analysis skills using IBM SPSS Statistics.

• YouTube
• 2 hours 31 minutes
• Self-Paced
• Free Video

Introduction to Machine Learning: Unsupervised Learning

12 ratings at Coursera

Explore unsupervised machine learning techniques for dimensionality reduction, clustering, and latent feature discovery. Apply these methods to real-world scenarios like recommender systems using Python.

• Coursera
• 14 hours 54 minutes
• Self-Paced
• Paid Course

Clustering Analysis

11 ratings at Coursera

Explore unsupervised learning techniques, including clustering methods and dimension reduction, to uncover patterns and insights in diverse datasets through hands-on tutorials and case studies.

• Coursera
• 1 day 13 hours 2 minutes
• Self-Paced
• Paid Course

Resampling, Selection and Splines

Explore advanced statistical learning techniques for data science, including resampling methods, model optimization, and non-linear approaches to enhance prediction accuracy and interpretability.

• Coursera
• 15 hours 56 minutes
• Self-Paced
• Paid Course