Unsupervised Learning Courses and Certifications

Whizlabs

Fundamentals of Machine Learning

Master machine learning fundamentals with hands-on experience in supervised/unsupervised learning, data preparation, model optimization, and cloud deployment on AWS and Azure.

• Coursera
• 12 hours 39 minutes
• Self-Paced
• Paid Course

Coursera

Detect AI Anomalies: Real-Time Outliers

Master statistical methods and unsupervised learning to build intelligent early warning systems that detect critical AI anomalies in real-time production environments.

• Coursera
• 2 hours 38 minutes
• Self-Paced
• Paid Course

Fundamental of Reinforcement Training

Discover reinforcement learning fundamentals through MDP concepts and hands-on demos, distinguishing RL from other ML approaches for real-world decision-making applications.

• Coursera
• 3 hours 4 minutes
• Self-Paced
• Paid Course

Artificial Intelligence for Marketing

Explore how AI, machine learning, and generative AI transform marketing—covering personalization, advertising, NLP, and strategies to champion AI initiatives in your organization.

• Coursera
• 3 hours
• Self-Paced
• Paid Course

EDUCBA

Customer Segmentation with K-Means: Model & Visualize

Master customer segmentation using K-Means clustering in Python to analyze shopping behavior, create visualizations, and derive actionable business insights from real-world data.

• Coursera
• 5 hours
• Self-Paced
• Paid Course

EDUCBA

Machine Learning in Python: Analyze & Apply

Master Python's data science ecosystem through hands-on machine learning fundamentals, NumPy, Matplotlib, Pandas, and scikit-learn with advanced applications like face recognition and sentiment analysis.

• Coursera
• 13 hours 53 minutes
• Self-Paced
• Paid Course

Packt

Clustering and Classification with Machine Learning in R

Master R for data analysis: from preprocessing to advanced machine learning algorithms. Gain practical skills in clustering, classification, and dimension reduction using real-world datasets and popular R packages.

• Coursera
• 11 hours 42 minutes
• Self-Paced
• Paid Course

EDUCBA

Excel: Apply & Evaluate Unsupervised Clustering

Master unsupervised clustering techniques using Excel's built-in functions to group data, create visualizations, and evaluate analytical outcomes without programming.

• Coursera
• 5 hours 13 minutes
• Self-Paced
• Paid Course

Packt

Algorithm Alchemy: Unlocking the Secrets of Machine Learning

Unlock machine learning secrets with Python implementation of supervised, unsupervised, and deep learning algorithms for real-world data problems.

• Coursera
• 5 hours 9 minutes
• Self-Paced
• Paid Course

H2O.ai

DataPrep for H2O Driverless AI

Master data preparation techniques for machine learning using H2O's Driverless AI, covering tabular formats, time series analysis, and automated preprocessing for both supervised and unsupervised learning scenarios.

• Coursera
• 1 hour 9 minutes
• Self-Paced
• Paid Course

EDUCBA

Machine Learning Projects in R with Caret

Master hands-on machine learning workflows in R using caret package through real-world projects covering data preprocessing, missing value handling, and clustering techniques.

• Coursera
• 5 hours 5 minutes
• Self-Paced
• Paid Course

EDUCBA

R: Apply & Analyze K-Means Clustering for Unsupervised ML

Master K-Means clustering in R through hands-on implementation, data preparation, and customer segmentation analysis for real-world unsupervised machine learning applications.

• Coursera
• 2 hours 34 minutes
• Self-Paced
• Paid Course

Coursera

ML Concepts, Models & Workflow Essentials

Master advanced machine learning in Java using Weka, Deeplearning4j, and Smile to build enterprise-grade predictive models, customer segmentation, and scalable MLOps solutions.

• Coursera
• 4 hours 36 minutes
• Self-Paced
• Paid Course

Packt

AI Demystified - A Simple Guide for Newcomers

Discover AI fundamentals from history to future trends, covering machine learning, deep learning, NLP, and transformers in this beginner-friendly guide.

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

Data Processing, Machine Learning, and Model Evaluation

Master data processing, machine learning modeling, and evaluation techniques—covering encoding, normalization, bias-variance trade-off, hyperparameter tuning, and MLOps deployment strategies for real-world applications.

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