t-SNE Courses and Certifications

Unsupervised Deep Learning in Python

2350 ratings at Udemy

Autoencoders, Restricted Boltzmann Machines, Deep Neural Networks, t-SNE and PCA

• Udemy
• 10 hours 8 minutes
• On-Demand
• Paid Course

Unsupervised Machine Learning : With 2 Capstone ML Projects

87 ratings at Udemy

Learn Complete Unsupervised ML: Clustering Analysis and Dimensionality Reduction

• Udemy
• 2 hours 59 minutes
• On-Demand
• Paid Course

Advanced Dimensionality Reduction in R

Learn how to apply advanced dimensionality techniques such as t-SNE and GLRM.

• DataCamp
• 4 hours
• On-Demand
• Free Trial Available

Data Science Foundations: Data Mining

304 ratings at LinkedIn Learning

Learn the key concepts and skills behind one of the most important elements of data science: data mining.

• LinkedIn Learning
• 3 hours 3 minutes
• On-Demand
• Free Trial Available

Dimensionality Reduction in R

Learn dimensionality reduction techniques in R and master feature selection and extraction for your own data and models.

• DataCamp
• 4 hours
• On-Demand
• Free Trial Available

Unsupervised Learning in Python

76 ratings at DataCamp

Learn how to cluster, transform, visualize, and extract insights from unlabeled datasets using scikit-learn and scipy.

• DataCamp
• 4 hours
• On-Demand
• Free Trial Available

Reducing Dimensions in Data with scikit-learn

19 ratings at Pluralsight

Learn powerful dimensionality reduction techniques in scikit-learn to optimize ML models, reduce overfitting, speed up training, and improve visualization. Covers feature selection, PCA, LDA, and manifold learning methods.

• Pluralsight
• 2-3 hours
• On-Demand
• Free Trial Available

Dimensionality Reduction in Python

11 ratings at DataCamp

Understand the concept of reducing dimensionality in your data, and master the techniques to do so in Python.

• DataCamp
• 4 hours
• On-Demand
• Free Trial Available

Intro to Unsupervised Machine Learning

Master unsupervised machine learning through hands-on practice with clustering, dimensionality reduction, and advanced algorithms using the Iris dataset for practical data analysis applications.

• CodeSignal
• On-Demand
• Paid Course

Non-linear Dimensionality Reduction Techniques

Master non-linear dimensionality reduction techniques like t-SNE, LLE, and Kernel PCA to uncover hidden patterns in complex datasets through hands-on visualization and parameter tuning.

• CodeSignal
• On-Demand
• Paid Course

Discover AI

Dimensional Reduction Using PaCMAP: From High-Dimensional Data to Vector Spaces - MNIST Case Study

Explore dimensional reduction using PaCMAP algorithm, from basic implementation to advanced applications with MNIST datasets, featuring hands-on Python coding in Colab notebooks.

• YouTube
• 14 minutes
• On-Demand
• Free Video

Microcredential

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
• On-Demand
• Paid Course
• 5 courses

Performing Dimension Analysis with R

This course covers virtually all of the important techniques of dimensionality reduction available in R, allowing model builders to optimize model performance by reducing overfitting and saving on model training time and cost.

• Pluralsight
• 2-3 hours
• On-Demand
• Free Trial Available

Roboflow

CLIP, T-SNE, and UMAP - Master Image Embeddings and Vector Analysis

Master image embeddings and vector analysis techniques like CLIP, T-SNE, and UMAP. Learn to cluster MNIST images, detect duplicates, and explore essential concepts in computer vision and data science through hands-on practice.

• YouTube
• 21 minutes
• On-Demand
• Free Video

Applied Algebraic Topology Network

Dimension Reduction - An Overview

Comprehensive overview of dimension reduction techniques, from linear methods like PCA to nonlinear approaches such as LLE, Isomap, t-SNE, and UMAP, with practical application to chemical reaction data.

• YouTube
• 57 minutes
• On-Demand
• Free Video