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ML-UL-EP2-Hierarchical Clustering [ ENGLISH ]

ML-UL-EP2-Hierarchical Clustering [ ENGLISH ]

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 🎙️ Episode Title: Hierarchical Clustering – Building Clusters from the Ground Up 🔍 Episode Description: Welcome to another thought-provoking episode of “Pal Talk – Machine Learning”, where we explore the fascinating world of data analysis and machine learning — one episode at a time! Today, we’re diving deep into a powerful unsupervised learning technique known as Hierarchical Clustering. If you’ve ever wanted to discover natural groupings in your data without predefining the number of clusters, then this method is for you. Think of it as creating a family tree of data points — step-by-step, layer-by-layer. In this episode, we explore: ✅ What is Hierarchical Clustering? Hierarchical Clustering is an unsupervised learning algorithm used to group data into clusters based on their similarity. Unlike K-Means, you don’t need to predefine the number of clusters — it builds a tree-like structure (dendrogram) to reveal how your data naturally groups together. ✅ Types of Hierarchical Clustering Agglomerative (Bottom-Up): Start with individual points and merge them into clusters. Divisive (Top-Down): Start with one large cluster and split it down. We break down both approaches and explain why Agglomerative Clustering is the most commonly used. ✅ How It Works – Step-by-Step Calculate the distance matrix Link the closest points or clusters using linkage criteria (single, complete, average, ward’s method) Repeat the merging process Visualize the results using a dendrogram We’ll guide you through each step with a fun and easy-to-understand example — like grouping animals based on their traits or students based on their test scores. ✅ Dendrograms Made Simple Learn how to read and interpret a dendrogram, and how to “cut the tree” to form meaningful clusters. ✅ Distance & Linkage Metrics From Euclidean and Manhattan distance to Ward’s Method and complete linkage, we explain how the choice of distance metric and linkage method influences your clustering results. ✅ When to Use Hierarchical Clustering You don’t know how many clusters to expect You want to visualize hierarchical relationships You have small to medium-sized datasets It’s perfect for bioinformatics, customer segmentation, text classification, and more. ✅ Hierarchical Clustering vs K-Means We compare both methods side-by-side, helping you understand the pros and cons of each. You’ll never confuse them again! ✅ Practical Applications Grouping genes based on expression profiles Organizing articles by topic similarity Segmenting customers with overlapping behavior patterns ✅ How to Implement It in Python (Brief Overview) We introduce how to use Scikit-learn and SciPy to create and visualize hierarchical clusters — with code you can try right away. 👥 Hosts: Speaker 1 (Male): A data science educator who makes algorithms relatable. Speaker 2 (Female): A hands-on learner turning questions into clarity for all. 🎧 Whether you're exploring machine learning, working in research, or just love discovering the hidden structure of data, this episode will give you the insights you need to understand and apply Hierarchical Clustering with confidence. 📌 Coming Soon on “Pal Talk – Machine Learning” DBSCAN: Density-Based Clustering Dendrograms vs Heatmaps Silhouette Score & Cluster Validation Principal Component Analysis (PCA) 💡 Like what you hear? Subscribe, rate, and share “Pal Talk – Machine Learning” and help us grow a community where numbers speak, and stories emerge from data. 🎓 Pal Talk – Where Data Talks.

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