Dimensionality Reduction Courses and Certifications

Feature Engineering for Text Classification

1 rating

Turn raw text into model-ready features: tokenize movie reviews, build Bag-of-Words and TF-IDF vectors, work with sparse matrices, and reduce dimensions with TruncatedSVD.

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

Matrix Factorization and Advanced Techniques

190 ratings at Coursera

Build recommender systems with matrix factorization: reduce the user-product preference space, then blend algorithms into hybrid and machine-learning recommenders.

• Coursera
• 6 weeks, 2 hours a week
• Self-Paced
• Paid Course

Unsupervised Machine Learning

372 ratings at Coursera

Cluster unlabeled data and reduce dimensions with k-means, distance metrics, PCA, kernel PCA, multidimensional scaling and matrix factorization, then compare models in a final project.

• Coursera
• 7 weeks, 3 hours a week
• Self-Paced
• Paid Course

Clustering Analysis

11 ratings at Coursera

Apply partitioning, hierarchical, density-based, and grid-based clustering plus PCA dimension reduction to datasets, evaluating results with performance metrics in a final case study.

• Coursera
• 6 weeks, 6 hours a week
• Self-Paced
• Paid Course

Data Processing and Manipulation

Handle missing values and outliers, apply sampling, dimensionality reduction, scaling and discretization, and build data cubes and pivot tables with Pandas.

• Coursera
• 4 weeks, 7 hours a week
• Self-Paced
• Paid Course

Advanced Machine Learning and its Applications

Develop and evaluate advanced AI solutions in Python using data preprocessing, neural network optimization, deep learning, and generative models including CNNs, LSTMs, GANs, and diffusion models.

• Coursera
• 5 weeks, 5 hours a week
• Self-Paced
• Paid Course

Machine Learning and Data Analytics Part 1

Apply data mining in engineering: run exploratory analysis, reduce dimensions with PCA and t-SNE, build classification and regression models, and evaluate them using RMSE and AUC-ROC.

• Coursera
• 7 weeks, 2 hours a week
• Self-Paced
• Paid Course

Practical Engineering Data Mining: Techniques and Uses

Apply data mining to engineering problems: explore and visualize data, reduce dimensions with PCA and t-SNE, and evaluate models with RMSE, F1-score, MCC and AUC-ROC.

• Coursera
• 4 weeks, 3 hours a week
• Self-Paced
• Paid Course

Coursera

Advanced Machine Learning Techniques

Implement bagging, boosting and stacking, reduce dimensions with PCA, t-SNE and UMAP, build NLP and reinforcement learning models, and automate tuning with AutoML and MLflow.

• Coursera
• 5 weeks, 7 hours a week
• Self-Paced
• Paid Course

Career Certificate

Feature Engineering

Learn to transform data into machine learning features, select useful features, and reduce dataset dimensionality using Python.

• Codecademy
• 6 hours
• Self-Paced
• Free Trial Available

Career Certificate

Intermediate Machine Learning

Builds intermediate machine learning skills through advanced supervised models, hyperparameter tuning, ensemble methods, dimensionality reduction, and a portfolio project.

• Codecademy
• 8 hours
• Self-Paced
• Free Trial Available

Machine Learning: Unsupervised Models

This course introduces key unsupervised machine learning techniques and explains how to choose the best algorithm for your data. Learn clustering and dimensionality reduction to find insights in unlab

• edX
• 1 week, 20-24 hours a week
• Self-Paced
• Free Online Course (Audit)

Shashank Kalanithi

Hands-On Machine Learning

Master practical machine learning through hands-on projects covering classification, SVMs, decision trees, ensemble methods, and unsupervised learning techniques.

• YouTube
• 5 hours 30 minutes
• Self-Paced
• Free Video

The Julia Programming Language

Julia for Data Science

Master Julia programming for data science through hands-on training in linear algebra, statistics, machine learning, neural networks, and visualization techniques.

• YouTube
• 4 hours 45 minutes
• Self-Paced
• Free Video

TensorFlow

A Friendly Introduction to Linear Algebra for ML

An informal tour of vectors, matrices, embeddings, and dimensionality reduction as the linear algebra behind machine learning.

• YouTube
• 38 minutes
• Self-Paced
• Free Video