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Master Simplified Unsupervised Machine Learning End to End ™
Real Life Projects Unlock Hidden Patterns and Insights
This Course Includes
udemy
0 (0 reviews )
10 total hours 
english
Online - Self Paced
course
Udemy
About Master Simplified Unsupervised Machine Learning End to End ™
Master Simplified Unsupervised Machine Learning™ is a comprehensive program designed to provide a deep dive into the techniques, algorithms, and applications of unsupervised learning in data science and machine learning. This course demystifies the complexity of unsupervised learning, covering everything from foundational concepts to advanced clustering methods, dimensionality reduction, and association rule mining. Learners will gain hands-on skills in detecting patterns, segmenting data, and uncovering hidden structures without labeled data, equipping them with powerful tools for real-world applications across diverse industries.
Course Overview
Course Format: Self-paced with instructor-led sessions
Target Audience: Data scientists, machine learning enthusiasts, and professionals seeking a deep understanding of unsupervised learning techniques
Key Learning Objectives
Understand the core principles of unsupervised learning and its applications
Master algorithms for clustering, anomaly detection, and dimensionality reduction
Gain practical experience with advanced methods like PCA, LDA, t-SNE, and DBSCAN
Apply association rule mining and the Apriori Algorithm for actionable data insights
Course Highlights
Anomaly Detection: Detect outliers and irregular patterns within large datasets
K-Means and Hierarchical Clustering: Techniques for segmenting data effectively
DBSCAN for Density-Based Clustering: Ideal for noisy and high-density datasets
Dimensionality Reduction with PCA and LDA: Reduce complexity while preserving essential data features
t-SNE Visualization: Transform complex data for intuitive 2D/3D visualizations
Association Rule Mining with Apriori Algorithm: Uncover hidden correlations and patterns
Course Curriculum
Introduction to Unsupervised Learning & Anomaly Detection
K-Means Clustering & Iterative Optimization
Advanced Clustering - Hierarchical Clustering and Dendrograms
DBSCAN - Density-Based Clustering and Applications
Principal Component Analysis (PCA) - Feature Extraction
Linear Discriminant Analysis (LDA) - Dimensionality Reduction Explained
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
With Master Simplified Unsupervised Machine Learning™, learners will be fully equipped to apply unsupervised techniques to uncover insights, drive decisions, and unlock the full potential of data.
Instructor
Our instructors are industry-leading AI/ML experts with years of experience in teaching, research, and real-world applications. They bring practical insights, hands-on skills, and industry best practices to make learning engaging and applicable.
What You Will Learn?
- Understanding the core principles and techniques of Unsupervised Learning..
- Mastering Anomaly Detection methods for identifying outliers in datasets..
- In-depth understanding and application of K-Means Clustering in Unsupervised Learning..
- Iterating and optimizing the K-Means Clustering Algorithm for better results..
- Practical applications of the K-Means Clustering algorithm in real-world scenarios..
- Mastering Hierarchical Clustering and understanding its advantages in Unsupervised Learning..
- Visualizing Hierarchical Clustering using Dendrograms for better interpretation..
- Applying Hierarchical Clustering to solve complex clustering problems..
- Learning the DBSCAN Algorithm and its effectiveness in density-based clustering..
- Exploring the advantages of DBSCAN in handling complex clustering patterns..
- Introduction to Principal Component Analysis (PCA) for dimensionality reduction..
- Selecting optimal components in PCA for reducing dimensionality effectively..
- Applying Principal Component Analysis (PCA) to real-world data for dimensionality reduction..
- Understanding Linear Discriminant Analysis (LDA) for unsupervised learning tasks..
- Comparing PCA vs. LDA in terms of dimensionality reduction and classification..
- Applying Linear Discriminant Analysis (LDA) for optimizing classification in unsupervised learning..
- Mastering t-SNE for advanced dimensionality reduction and visualization of high-dimensional data..
- Understanding how t-SNE works and using it effectively for data visualization..
- Practical applications of t-SNE in reducing dimensions and visualizing complex datasets..
- Exploring various unsupervised learning model evaluation metrics for clustering algorithms..
- Understanding and applying dimensionality reduction evaluation metrics for model assessment..
- Learning techniques for hyperparameter tuning in unsupervised learning models..
- Using Bayesian Optimization for improving the performance of unsupervised learning models..
- Introduction to Association Rule Mining for market basket analysis and beyond..
- Understanding confidence and support in Association Rule Mining for actionable insights..
- Learning the Apriori Algorithm for efficient Association Rule Mining and market basket analysis..
- Step-by-step application of the Apriori Algorithm to uncover valuable patterns in data..