What will you learn in Cluster Analysis and Unsupervised Machine Learning in Python Course
Master K-Means Clustering, its limitations, and extend it to soft (fuzzy) K-Means implementations.
Understand and implement Hierarchical Clustering methods, including dendrogram interpretation and linkage strategies (single, complete, Ward, UPGMA).
Learn Gaussian Mixture Models (GMMs) and the Expectation-Maximization (EM) algorithm—when GMMs align with K-Means and how they address its weaknesses.
Apply Kernel Density Estimation (KDE) for density estimation and pattern discovery.
Program Overview
Module: Fundamentals & K-Means Clustering
⏳ ~2 hours
Topics: Introduction to unsupervised learning, the mechanics of standard and soft K-Means, drawbacks of cluster separation, initialization strategies.
Hands‑on: Implement K-Means manually and with libraries, and visualize clusters using Matplotlib/seaborn.
Module: Hierarchical Clustering & Linkage Methods
⏳ ~1.5 hours
Topics: Agglomerative clustering algorithms, linkage types, dendrogram construction, and cluster extraction.
Hands‑on: Use SciPy to cluster sample datasets and generate dendrogram visualizations.
Module: Gaussian Mixture Models & EM
⏳ ~2 hours
Topics: Understand EM convergence, covariance constraints, density estimation, and how GMM relates to K-Means.
Hands‑on: Code EM-based clustering from scratch; compare results against K-Means clustering.
Module: Kernel Density Estimation & Evaluations
⏳ ~1 hour
Topics: Introduce KDE for unsupervised density estimation and model evaluation techniques.
Hands‑on: Apply KDE using SciPy; compare estimated density plots to real data distributions.
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Job Outlook
Strongly relevant for roles like Data Analyst, Data Scientist, or ML Engineer, particularly where pattern detection from unlabeled data is required.
Cluster analysis and unsupervised learning skills are in demand in sectors such as marketing segmentation, anomaly detection, recommendation systems, and exploratory data science.
Acts as foundational know-how for advanced ML pipelines, making you better suited for roles involving feature extraction, data preprocessing, or research-oriented exploratory modeling.
Salary estimates: Analytics roles with machine learning capacities often pay ₹8L–20L/year in India and $90K–$140K/year in the U.S.
Specification: Cluster Analysis and Unsupervised Machine Learning in Python
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