Unsupervised Machine Learning Training – KHDA Approved Program
Master the essentials of unsupervised machine learning from clustering and dimensionality reduction to anomaly detection and recommender systems. With our KHDA-approved training program, you can learn to discover patterns in unlabeled data, group datasets, and extract actionable insights aligned with real-world business and technology requirements.
Course Duration: 15 Weeks – 20 Weeks
Delivery Mode:Classroom / Online / Blended
Unsupervised Machine Learning Training – Program Overview
The Unsupervised Machine Learning Training Program is a 15–20 week (60-hours) practical course designed to develop professionals who can analyze complex datasets without labels and identify hidden structures and patterns. Learners gain hands-on experience with clustering algorithms, dimensionality reduction, anomaly detection, association rule learning, and recommender systems through real-world datasets and simulations. The program emphasizes applied skills and workflow-relevant techniques to ensure learners are job-ready for roles in AI, data analytics, and data-driven business solutions. Graduates can pursue roles such as Data Analyst, Data Scientist, and ML Engineer across Dubai’s rapidly growing technology ecosystem.
Unsupervised Machine Learning Training in Dubai – Who is it for?
- Aspiring Data Scientists and Machine Learning Engineers
- Software Developers and Programmers
- Data Analysts
- Domain Experts in Technology and Healthcar
Skills You’ll Develop with Our Cybersecurity Course in Dubai
Gain in-demand technical and professional skills that prepare you to excel in cybersecurity roles across industries.
Clustering & Segmentation
Learn to group data and discover patterns using K-Means, Hierarchical, and DBSCAN methods.
Master PCA, t-SNE, and other techniques to simplify large datasets while preserving key information.
Anomaly Detection
Identify outliers and unusual patterns, useful in fraud detection, cybersecurity, and operational monitoring.
Recommender Systems & Association Rules
Build systems to deliver personalized recommendations and understand customer behavior
Software, Tools, Languages & Frameworks
Unsupervised Machine Learning – Course Modules Total Duration: 60 Hours
Module 1: Fundamentals of Machine Learning
Topics Covered:
- Supervised vs Unsupervised Learning
- Types of ML problems and workflows
- Distance metrics and similarity measures
- Bias, variance, and model evaluation basics
- Data preprocessing and feature scaling
Module 2: K-Means Clustering
Topics Covered:
- Centroid-based clustering concept
- K-Means algorithm step-by-step
- Choosing optimal K (Elbow Method, Silhouette Score)
- Initialization strategies
- Practical implementation using Python
Module 3: Hierarchical Clustering
Topics Covered:
- Agglomerative vs Divisive clustering
- Linkage methods (single, complete, average)
- Dendrogram interpretation
- Distance matrix computation
- Real-world clustering case study
Module 4: DBSCAN
Topics Covered:
- Density-based clustering fundamentals
- Core points, border points, noise
- Epsilon and MinPts parameter tuning
- Handling outliers
- DBSCAN implementation and visualization
Module 5: Gaussian Mixture Models (GMM)
Topics Covered:
- Probabilistic clustering approach
- Gaussian distributions and covariance
- Expectation-Maximization (EM) algorithm
- Soft clustering vs hard clustering
- Model comparison with K-Mean
Module 6: Dimensionality Reduction
Topics Covered:
- Curse of dimensionality
- Principal Component Analysis (PCA)
- Eigenvalues and variance explanation
- Feature selection vs feature extraction
- Visualization of high-dimensional data
Module 7: Anomaly Detection
Topics Covered:
- Concept of outliers and rare events
- Statistical and distance-based methods
- Isolation Forest basics
- Use cases in fraud and cybersecurity
- Evaluating anomaly detection models
Module 8: Association Rule Learning
Topics Covered:
- Market basket analysis
- Support, Confidence, Lift metrics
- Apriori algorithm
- Rule generation and pruning
- Business use case implementation
Module 9: Recommender Systems
Topics Covered:
- Collaborative vs Content-based filtering
- Similarity measures
- Matrix factorization basics
- Cold start problem
- Practical recommendation engine build
Module 10: Practical Application and Projects
Topics Covered:
- End-to-end unsupervised ML workflow
- Real-world dataset analysis
- Model comparison and evaluation
- Business case presentation
- Capstone project with deployment-ready output
