Dimensionality Reduction Courses and Certifications

Navigating Data Simplification with PCA

Master dimensionality reduction techniques using Principal Component Analysis in R, from understanding eigenvectors to implementing PCA for feature extraction and classification tasks.

• 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

Feature Engineering for Text Classification

1 rating

Master text classification techniques through hands-on practice with tokenization, Bag-of-Words, TF-IDF, sparse matrices, and dimensionality reduction for effective natural language processing applications.

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

Natural Language Processing with Classification and Vector Spaces

4638 ratings at Coursera

This is the Course 1 of the Natural Language Processing Specialization, offered by deeplearning.ai

• Coursera
• 1 day 9 hours 23 minutes
• Self-Paced
• Paid Course

Materials Data Sciences and Informatics

351 ratings at Coursera

Explore materials informatics at the intersection of materials science, computational science, and information science. Learn data-driven approaches to accelerate materials development and deployment.

• Coursera
• 9 hours 10 minutes
• 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

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

Matrix Factorization and Advanced Techniques

190 ratings at Coursera

Explore matrix factorization and hybrid techniques for recommender systems, covering dimensionality reduction, algorithm combination, and advanced machine learning methods for powerful recommendations.

• Coursera
• 14 hours 11 minutes
• Self-Paced
• Paid Course

Data Visualization for Genome Biology

25 ratings at Coursera

Explore genome biology data visualization through hands-on labs using web tools and R. Learn theoretical concepts, practical techniques, and best practices for creating informative biological data plots.

• Coursera
• 15 hours 20 minutes
• Self-Paced
• Paid Course

Unsupervised Machine Learning

367 ratings at Coursera

Explore unsupervised machine learning techniques, including clustering and dimensionality reduction, to uncover insights from unlabeled data. Gain hands-on experience with algorithms and best practices.

• Coursera
• 22 hours 41 minutes
• Self-Paced
• Paid Course

Deep Learning and Reinforcement Learning

299 ratings at Coursera

Explore neural networks, deep learning architectures, and reinforcement learning. Gain hands-on practice with Keras, CNNs, transfer learning, RNNs, autoencoders, and generative models for AI applications.

• Coursera
• 1 day 7 hours 22 minutes
• Self-Paced
• Paid Course

Career Certificate

Data Analysis with Python

29 ratings at Coursera

Comprehensive overview of data analysis techniques using Python, covering classification, regression, clustering, and more. Includes hands-on examples and a final project.

• Coursera
• 8 weeks, 10 hours/week
• Self-Paced
• Paid Course
• 5 courses

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

Introduction to Machine Learning: Unsupervised Learning

10 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

Data Analysis with Python Project

Comprehensive exploration of data analysis techniques, from classification to outlier detection, empowering learners to conduct real-world projects and make data-driven decisions.

• Coursera
• 18 hours 11 minutes
• Self-Paced
• Paid Course