k-Nearest Neighbors (kNN) and Clustering : ONE-SHOT GATE DA | Concept to Combat Session 5 🔥

Welcome to Session 5 of the Concept to Combat series! This is your ultimate one-stop resource for mastering some of the most high-weight and frequently tested topics in the GATE DA Machine Learning syllabus: k-Nearest Neighbors (kNN) and Clustering. What you'll learn: In this session, we will do a recap of the concepts and then go in deep dive for extensive practice of GATE-level problems. We don’t just stop at the theory; we ensure you can apply every formula and geometric intuition to solve complex questions under pressure. 📘 Click the link below to download all session PDFs: drive.google.com/drive/folders/1f7sZIS4-icsxaHEB1i… Join our Communities for Notes: Telegram: t.me/ManojGateDA Discord: discord.com/invite/AwZqYz9wvK ML Module Schedule: Jan 20 – Feb 1. Subscribe to TAAI- Manoj Kumar to join the combat live! Jump to Topics: [00:00] – Session Start: Introduction to the topics (kNN and Clustering) and their weightage in exams. [00:54] – Concept to Combat Series Roadmap: Review of previous sessions (Linear Regression, SVM, Decision Trees) and what’s coming next (PCA, LDA, Neural Networks). [02:50] – Core Concept: Introduction to kNN as a supervised, non-parametric algorithm used for both classification and regression. [04:51] – Mechanism: Explaining prediction based on "similarity" or "closeness" using distance metrics. [06:34] – kNN for Regression: Using the average of y-values of neighbors for prediction. [07:15] – Practice Question 1: Solving a 2D space classification problem using 3-NN and Euclidean distance. [12:03] – Practice Question 2: Finding labels for specific points in a binary classification problem. [14:31] – Leave-One-Out Cross Validation (LOOCV) [17:15] – GATE DA 2024 Question: Determining the minimum odd value of k to assign a specific label. [20:04] – Voronoi Diagrams: Understanding decision boundaries for k=1and how the boundary smooths as k increases. [21:58] – Computational Cost: Why kNN is called a "Lazy Learner" (zero training time, high test-time complexity). [27:34] – Intro to Unsupervised Learning: Identifying hidden structures in unlabeled data (e.g., customer segmentation). [32:30] – Hierarchical (Agglomerative) Clustering: [34:49] – Linkage Criteria: [35:10] – Single Linkage Walkthrough: Step-by-step distance matrix update and merging process. [39:36] – Dendrograms: How to interpret the y-axis (dissimilarity) and represent merges visually. [44:24] – Transition to Complete Linkage: [48:30] – Final Matrix Updates: [51:09] – Building the Complete Linkage Dendrogram: [56:54] – Vertical vs. Horizontal Axis: [57:51] – Reordering Nodes: [59:27] – Practice Question Start: Analyzing a specific dendrogram to identify which cluster configuration corresponds to the hierarchy. [59:40] – Step-by-Step Solution: [01:00:06]: Identifying the first merge between A and C. [01:04:03] – GATE DA 2025 (Sample/Predicted) Question: Definitions of dissimilarity between sets of objects. [01:06:06] – K-Means Mechanism: Introduction to fixed $k$, random initialization, cluster assignment, and centroid update steps. [01:08:44] – Convergence: When centroids stop moving between iterations [01:10:09]. [01:10:46] – Step-by-Step Numerical: One complete iteration of K-Means (assignment then update). [01:14:32] – GATE DA 2024 Question: Understanding Euclidean-based K-Means and the property of points along the line joining data points. [01:18:09] – Optimization Objective: Explaining why we use the "Mean" (it minimizes the sum of squared distances) vs. "Median" (minimizes absolute differences) [01:23:38] – Weighted K-Means [01:30:48] – Sensitivity to Initialization [01:34:14] – Final Closing #GATEDA #MachineLearning #LinearRegression #TAAI #ConceptToCombat GATE DA 2026 GATE Data Science AI GATE 2026 preparation GATE DA free course GATE DA practice questions GATE DA MCQ MSQ NAT GATE DA strategy GATE DA toppers preparation GATE DA machine learning Linear algebra for GATE DA Probability for GATE DA GATE DA exam tips GATE DA concepts and questions GATE DA next level preparation TAAI GATE Tomorrow's Architect of AI GATE DA rank oriented GATE DA 2026 full syllabus GATE DA beyond concepts To check out the course- www.taai.live/ Join our complete course to boost your GATE DA preparation. 🔹About the complete course: ✅ Complete syllabus coverage for GATE DA. ✅ Concept-focused lectures + regular doubt sessions. ✅ Subject-specific doubt channels. ✅ Expert guidance from our faculty (Manoj Sir, AIR-13 and Sahitya Sir). This course is for anyone who wishes to crack GATE DA, whether you're an absolute beginner or a pro. Join our community: 📌 Website: www.taai.live/ 📌 Telegram: t.me/Manoj_Gate_DSAI 📌 Discord: discord.gg/Yj7pSxMT 📌LinkedIn: www.linkedin.com/company/taai-gate-da-placements/?… 🔔 Subscribe to our channel and hit the bell icon to get more u