Master Simplified Unsupervised Machine Learning End to End ™
Real-World Case Studies and Practical Applications
Updated on Jun, 2025
Language - English
IT and Software ,Other IT and Software,Python
Lectures -39
Duration -13 hours
Lifetime Access
Lifetime Access
30-days Money-Back Guarantee
Get your team access to 10000+ top Tutorials Point courses anytime, anywhere.
Course Description
Master Simplified Unsupervised Machine Learning is the most comprehensive program in terms of techniques, algorithms, and applications of unsupervised learning in data science and machine learning. This course is a marathon that dissects the mystery surrounding unsupervised learning-from foundational concepts to advanced clustering methods, dimensionality reduction, and association rule mining. By the end of the course, learners will have learned hands-on to detect patterns, segment data, and identify hidden structures without labeled data. In this manner, they will be equipped with handy tools useful for application in different industries.
Course Overview
Course Type: Self study but instructor support sessions are included
Who Should Attend: Data scientists, machine learning enthusiasts and anyone interested in learning about unsupervised learning techniques at an advanced level
Key Take-Aways
Familiarization of the main concepts of unsupervised learning including its applications.
Learning of the following algorithms on clustering, anomaly detection, and dimensionality reduction.
Get experiential learning on PCA, LDA, t-SNE, and DBSCAN
Apply association rule mining and Apriori Algorithm for discovering data-mined insights
Course Outline
Anomaly Detection
Identify outliers and anomalies that emerge in a large dataset.
K-means and Hierarchical Clustering
Partition data into clusters or groups as best as possible
DBSCAN for Density-Based Clustering
Is a tool for the detection of clusters in noisy and high-density datasets.
Dimensionality Reduction with PCA and LDA
Reduce the complexity of a high-dimensional dataset keeping the key characteristics intact.
t-SNE Visualization: Transform complex data for intuitive 2D/3D visualizations
Association Rule Mining using Apriori Algorithm: Identifying unknown patterns and relationships
Course Outline
Introduction to Unsupervised Learning and Anomaly Detection
K-Means Clustering & Iterative Optimization
Advanced Clustering - Hierarchical Clustering and Dendrograms
DBSCAN - Density-Based Clustering and its Applications
Principal Component Analysis (PCA) - Feature Extraction
Linear Discriminant Analysis (LDA) - Explaining Dimensionality Reduction
t-SNE for Data Visualization and Dimensionality Reduction
Model Evaluation and Hyperparameter Tuning in Unsupervised Learning
Association Rule Mining - Market Basket Analysis, Confidence & Support
Apriori Algorithm - Step-by-Step Explanation and Practical Applications
Master Simplified Unsupervised Machine Learning will help learners make use of unsupervised techniques to find insights that would drive decisions and unlock the full potential of the data.
Instructor
Instruction Our instructors are industry leaders in AI/ML with a decade of experience teaching, researching, and implementing solutions. They bring such prudence knowledge, hands-on skills, and industry best practices so that learning is interactive and practical.
Goals
- Understand core principles and techniques of Unsupervised Learning.
Work with methods of Anomaly Detection: Outliers in datasets
Understand and work with K-Means Clustering of Unsupervised Learning
Iterative optimization of the K-Means Clustering algorithm to result in better outcomes
Practical applications of the K-Means Clustering algorithm on real-world applications
Work with Hierarchical Clustering and its advantages in Unsupervised Learning
Visualization with Dendrograms for hierarchical clustering
Application of Hierarchical Clustering towards the solution of complicated clustering problems.
Learning the DBSCAN Algorithm and its Strength in Density-Based Clustering
Discover the Strengths of DBSCAN in Dealing with Clustering Patterns
Introduction to PCA
Choosing Appropriate Features with PCA for Dimensionality Reduction
PCA Applied to Real Data of Dimensionality Reduction
Understanding Linear Discriminant Analysis for Unsupervised Learning
Comparing PCA vs LDA of Dimensionality Reduction and Classification.
Applying linear discriminant analysis on optimal classification in unsupervised learning
Mastering t-SNE for advanced dimensionality reduction and high-dimensional data visualization
Knowing how t-SNE works and applying it well for data visualization
Practical application of t-SNE in reducing dimensions for visualizing complex datasets
Exploring metrics for Evaluating various unsupervised learning models in clustering.
Understand and apply the evaluation metrics for assessing multidimensional reduction for model assessment
Techniques for hyperparameter tuning on unsupervised learning models.
Improving Performance of the Unsupervised Learning Model with Bayesian Optimization
Introduction to Association Rule Mining: Market Basket Analysis and Beyond
Understanding Confidence and Support in the Association Rule Mining for Actionable Insights
Learning the Apriori Algorithm for Effectual Association Rule Mining and Market Basket Analysis
Appling the Step-by-Step Approach of the Apriori Algorithm for Discovering Valuable Patterns in Data.
Prerequisites
Anyone can learn this class with simplicity
Anyone who wants to learn future skills and become Data Scientist, Sr. Data Scientist, Ai Scientist, Ai Engineer, Ai Researcher & Ai Expert.
Curriculum
Check out the detailed breakdown of what’s inside the course
Unsupervised Learning Explained | Clustering & Dimensionality Reduction
1 Lectures
-
Unsupervised Learning Explained | Clustering & Dimensionality Reduction
29:25
29:25
Unsupervised Learning Explained- Anomaly Detection
1 Lectures
Mastering K-Means Clustering in Unsupervised Learning
1 Lectures
Iterating K-Means Clustering Algorithm in Unsupervised Learning
1 Lectures
Application of K-Means Clustering Algorithm in Unsupervised Learning
1 Lectures
Mastering Hierarchical Clustering in Unsupervised Learning
1 Lectures
Unsupervised Learning Dendrogram Visualization
1 Lectures
Application Hierarchical Clustering Explained- Master Unsupervised Learning
1 Lectures
Advanced Clustering Techniques Unsupervised Learning with DBSCAN
1 Lectures
Advanced Clustering Techniques- Unsupervised Learning with DBSCAN Advantages
1 Lectures
Application Advanced Unsupervised Learning with DBSCAN Algorithm
1 Lectures
Introduction to Principal Component Analysis (PCA
1 Lectures
Selecting Principal Component Analysis (PCA) | Machine Learning
1 Lectures
Application of Principal Components in PCA | Machine Learning
1 Lectures
Unsupervised Learning with Linear Discriminant Analysis (LDA)
1 Lectures
PCA vs LDA | Machine Learning Dimensionality Reduction Explained
1 Lectures
Application of LDA | Machine Learning Dimensionality Reduction Explained
1 Lectures
Unsupervised Learning with t-SNE- Mastering Dimensionality Reduction
1 Lectures
Unsupervised Learning- How t-SNE Works - Mastering Dimensionality Reduction
1 Lectures
Application of t-SNE- Mastering Dimensionality Reduction
1 Lectures
Unsupervised Learning Model Evaluation Metrics- A Complete Guide
1 Lectures
Dimensionality Reduction Evaluation Metrics
1 Lectures
Unsupervised Learning Hyperparameter Tuning- A Complete Guide
1 Lectures
Unsupervised Learning with Bayesian Optimization- A Complete Guide
1 Lectures
Introduction to Association Rule Mining Market Basket Analysis & Beyond
1 Lectures
Association Rule Mining- Confidence & Support Explained
1 Lectures
Apriori Algorithm Association Rule Mining & Market Basket Analysis
1 Lectures
Apriori Algorithm Step-by-Step Explained
1 Lectures
FP-Growth Algorithm Explained | Uncover Market Basket Patterns
1 Lectures
FP-Growth Algorithm- Step-by-Step Exploration | Master Frequent Pattern Mining
1 Lectures
Model Evaluation Metrics for Association Rule Mining
1 Lectures
Leverage and Certainty Factor in Association Rule Mining
1 Lectures
Application of Association Rules in Data Science
1 Lectures
What is Reinforcement Learning?
1 Lectures
What is Deep Reinforcement Learning?
1 Lectures
Markov Decision Processes (MDPs) in Reinforcement Learning
1 Lectures
Solving Markov Decision Processes (MDPs) with Dynamic Programming
1 Lectures
Introduction to Q- learning Algorithm
1 Lectures
Applications of Q-Learning Algorithm
1 Lectures
Instructor Details
Dr. Noble Arya
Warm greetings.
I am Dr. Noble Arya, a Full-Stack Data Scientist, AI/ML Researcher, and Product Innovator with extensive experience across leading global organizations, including General Electric (GE) and Wipro Technologies.
As the founder of NobleX Infinity Labs®️, a globally recognized platform with prestigious TM® certification, I have had the privilege of mentoring over 100,000 students and 500+ educators across 170 countries. Our educational programs consistently maintain an average course rating of 4.7 out of 5 stars, as rated by more than 100,000 learners.
Academically, I hold a Honorary Doctorate in Artificial Intelligence and Machine Learning, and I am a certified graduate of the Post Graduate Program in Artificial Intelligence and Machine Learning from The University of Texas at Austin, in collaboration with Great Learning. My journey also includes 15+ years of dedicated research and application in AI/ML, Data Science, Deep Learning, and Value Innovation, with a strong grounding in the principles of Pure Consciousness.
I have been honored with over 300 national and international awards in recognition of my contributions to Future Skills, Creativity, Technological Innovation, and Ethical AI. My areas of expertise include Computer Science, Artificial Intelligence, Design Thinking, Super Pure Consciousness, and Value Innovation—acquired through both formal training and self-directed study.
Over the years, I have collaborated with prestigious institutions and organizations such as the Himalayan Institute of Alternatives (Ladakh), Teach for India, Harvard Medical School, University of Texas at Austin, and Toastmasters International. As an entrepreneur, I have successfully scaled two startups from grassroots family initiatives to nationally and internationally recognized enterprises.
My skillset encompasses a broad spectrum, including:-
Real-World Digital and Future Skills
Pure Consciousness and Value Technology
Project Management and Entrepreneurship
Artificial Intelligence and Computer Science
Data Science, Machine Learning, Deep Learning, Design Thinking and Product Development
Having completed over 100 projects in the past decade and a half, I am now committed to democratizing access to transformative education. Through my Udemy courses and YouTube channel, I aim to positively impact millions of learners. My vision for the coming decade is to empower 7 billion people worldwide, both online and offline, with real-life, problem-solving capabilities and ethical applications of AI—grounded in consciousness and compassion.
I invite you to join me on this transformative journey. Follow my work on Udemy and YouTube, and let us build a future where knowledge, technology, and human values uplift all 7+ billion people across the globe.
With deep gratitude to the Creator and to every atom and molecule across all universes, from the beginning to infinity.
With sincere regards,
Dr. Noble Arya
Full-Stack Data Scientist | AI/ML Researcher | Product Innovator
With gratitude till infinity,
Course Certificate
Use your certificate to make a career change or to advance in your current career.
Our students work
with the Best
Related Video Courses
View MoreAnnual Membership
Become a valued member of Tutorials Point and enjoy unlimited access to our vast library of top-rated Video Courses
Subscribe now
Online Certifications
Master prominent technologies at full length and become a valued certified professional.
Explore Now