PickAClass · AI, Tech & Engineering · AI & Machine Learning

Unsupervised Learning

Discover hidden patterns and structures in unlabeled data. Explore techniques like clustering, dimensionality reduction, and association rule mining.

60 courses

Unsupervised Machine Learning and Clustering in Python

Discover how to find hidden patterns in unlabeled data using k-means, hierarchical clustering, and density estimation with practical Python implementations.

★ 4.7 (5,236)

Machine Learning in R: Theory and Practice of Predictive Modeling

Master supervised and unsupervised machine learning algorithms in R, from foundational theory to building predictive models and clustering workflows.

Pattern Search Optimization in MATLAB for Beginners

Master the essentials of direct search algorithms and solve complex optimization problems using MATLAB and the Global Optimization Toolbox.

Python Data Science: Unsupervised Machine Learning Foundations

Master unsupervised learning techniques in Python to discover hidden patterns, detect anomalies, and build intelligent recommendation systems.

Unsupervised Machine Learning: Clustering in R and SAS

Learn to segment data and find hidden patterns using hierarchical and K-means clustering with step-by-step programming guides in SAS and R.

Cluster Analysis and Unsupervised Learning with Python

Discover hidden patterns and structures in unlabeled datasets using Python, Pandas, and essential unsupervised machine learning algorithms.

Unsupervised Learning, Recommender Systems, and Reinforcement Learning

Learn to group unlabeled data, build personalized recommendation engines, and train autonomous decision-making agents through clear, text-based lessons.

★ 4.9 (5,603)

Mathematical Foundations of PCA for Machine Learning

Master the linear algebra and statistics behind Principal Component Analysis to reduce data dimensionality and prepare high-dimensional features for machine learning models.

★ 4.0 (3,182)

Data Mining Foundations: Pattern Discovery and Text Analysis

Learn how to uncover hidden patterns in structured and unstructured data, analyze text, and extract actionable insights using modern data techniques.

★ 4.5 (2,949)

Machine Learning for Document Clustering and Retrieval

Master the fundamentals of grouping similar data, scaling search queries, and implementing modern clustering algorithms and vector retrieval in Python.

★ 4.7 (2,369)

Foundations of Data Mining and Knowledge Discovery

Learn to extract meaningful patterns from raw information using essential algorithms and modern data exploration techniques.

★ 4.6 (1,323)

Python Unsupervised Learning: Clustering and Pattern Discovery

Master the art of finding hidden patterns in unlabeled data using scikit-learn to cluster information and reduce dimensionality.

★ 4.8 (1,059)

Text Mining & Analytics: Statistical Methods for Beginners

Master foundational text mining techniques to extract valuable information and support data-driven decisions from diverse text sources.

Data Science Fundamentals: K-Means Clustering with Python

Discover how to group complex data into meaningful clusters using Python to uncover patterns for marketing, research, and business analysis.

Unsupervised Machine Learning with K-Means Clustering

Learn to discover hidden patterns in unlabeled data using Python, Pandas, and Scikit-Learn to build and evaluate your first clustering models.

Practical Cluster Analysis and Unsupervised Learning

Group unstructured data effectively using k-means, hierarchical, and density-based clustering algorithms while mastering modern validation techniques.

Support Vector Machines (SVM) for Machine Learning Beginners

Learn how to build, tune, and evaluate robust classification and regression models using Support Vector Machines and modern Python libraries.

Unsupervised Machine Learning: Clustering and Dimensionality Reduction

Discover hidden patterns in unlabeled data using Python, clustering algorithms, and dimensionality reduction techniques to drive real-world business insights.

Pattern Discovery and Data Mining for Large Datasets

Master the fundamental concepts of data mining and learn how to extract meaningful, scalable patterns from complex datasets using modern analytical techniques.

Learning Probabilistic Graphical Models from Data

Learn to estimate parameters and discover structures in complex probability distributions to build robust models for real-world uncertainty.

Showing 20 of 60 courses