Machine Learning

🎓 Complete Machine Learning Playlist | Algorithms & Solved Examples Welcome to the Ultimate Machine Learning Playlist – a detailed collection of tutorials covering a wide range of algorithms with step-by-step solved numerical examples. Whether you're a student, a beginner, or preparing for interviews, this playlist will help you master both the concepts and practical applications. --- 📘 Algorithms & Topics Covered: 🔸 Supervised Learning 📌 Decision Trees (ID3, Gini Index, C4.5, CART, Boolean Function Trees) 📌 Naive Bayes Classifier (Laplace Correction, Text Classification) 📌 K-Nearest Neighbors (KNN with various distance metrics) 📌 Linear & Logistic Regression (Simple, Multiple, Matrix Form) 📌 Support Vector Machines (SVM, Linear/Non-Linear, Kernel Methods) 📌 Neural Networks (Perceptron, Backpropagation, MLP, RBF, Activation Functions) 🔸 Unsupervised Learning 📊 K-Means Clustering (Standard, K-Means++, Bisecting K-Means) 📊 K-Medoids Clustering 📊 Hierarchical Clustering (Single, Average, Complete Linkage) 📊 DBSCAN, EM Algorithm, DIANA Clustering 📊 Dimensionality Reduction (PCA, SVD, KL Transform) 🔸 Ensemble & Advanced Methods 🌲 Random Forest 💡 Genetic Algorithms (Selection, Crossover, Mutation) 🔍 Bayesian Belief Networks 📈 Gradient Descent (Batch, Stochastic, Delta Rule) 🧪 Feature Selection (Wrapper, Filter, Embedded) 🔸 Other Key Concepts 🧠 Overfitting & Regularization (Lasso, Ridge, Elastic Net) 🎯 ROC Curve, Confusion Matrix, Accuracy, Precision, Recall, F1 Score 🔁 Distance Measures (Euclidean, Manhattan, Minkowski, Cosine, Hamming) 📊 Outlier Detection (IQR, Z-Score, SMOTE, NearMiss, Tomek Links) 🧾 Association Rules (Apriori, ECLAT) 🧮 Case-Based Reasoning, Candidate Elimination, Find-S 💬 TF-IDF, Sentiment Analysis, One-Hot Encoding --- 📝 Why this playlist? * Covers syllabus-relevant content for B.E., M.Tech, and competitive exams * Focused on conceptual clarity and hands-on problem solving * Great for quick revisions or full-length study 🔔 Subscribe and turn on notifications to stay updated!

Curated by: Mahesh Huddar (337 videos)


Currently Playing: Complete Linkage Hierarchical Clustering Agglomerative Clustering in ML by Vidya Mahesh Huddar

Complete Linkage Hierarchical Clustering Agglomerative Clustering in Machine Learning by Vidya Mahesh Huddar Single inkage CLustering: https://youtu.be/-oPyDp1IpV4 Complete inkage CLustering: https://youtu.be/ADRRbUSoIAc In this video, I explain Complete Linkage Hierarchical Clustering using the Agglomerative Method with a fully solved numerical example. We start by calculating the pairwise Euclidean distance matrix, then apply the Complete Linkage (Maximum Distance) rule step-by-step to merge clusters. Finally, we construct the dendrogram and understand how clusters are formed at each stage. This video is very useful for: Machine Learning students VTU / University exam preparation Data Mining learners Interviews and viva questions Topics Covered: Agglomerative Hierarchical Clustering Euclidean Distance Calculation Complete Linkage Method (Maximum Distance) Distance Matrix Formation Cluster Merging Steps Dendrogram Construction Step-by-step Numerical Problem ******************************** Follow Us on: 1. Blog / Website: https://www.vtupulse.com/ 2. Download Final Year Project Source Code: https://vtupulse.com/download-final-year-projects/ 3. Like Facebook Page: https://www.facebook.com/VTUPulse 4. Follow us on Instagram: https://www.instagram.com/vtupulse/ 5. Like, Share, Subscribe, and Don't forget to press the bell ICON for regular updates


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