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: 2. DIANA Clustering Solved Example Divisive Analysis Clustering Solved Example by Vidya Mahesh Hudar

DIANA Clustering Solved Example Divisive Clustering Divisive Analysis Clustering Solved Example by Vidya Mahesh Hudar Given the dataset {A, B, C, D, E} and the distance matrix in Table. Apply divisive analysis clustering algorithm (DIANA) to form clusters. KMeans Clustering Algorithm: https://youtu.be/3tLdD-4-wnI K Means Solved Example: https://youtu.be/KzJORp8bgqs K MEans++ Solved Example: https://youtu.be/z2yncM2HE6M Fuzzy C Means Solved Example: https://youtu.be/X7co6-U4BJY K MEdoid Solved Example: https://youtu.be/ChBxx4aR-bY DBScan Solved Example 1: https://youtu.be/-p354tQsKrs DBScan Solved Example 2: https://youtu.be/ZOLYaa9Jex0 Agglomerative Hierarchical Clustering: https://youtu.be/YH0r47m0kFM DIANA Clustering Algorithm: https://youtu.be/fpzZ_adkNKQ DIANA CLustering example 1: https://youtu.be/jcdT_pVRqlE DIANA CLustering example 2: https://youtu.be/uU4jvS8sqZY The following concepts are discussed: ______________________________ DIANA Solved Example, DIANA Clustering Solved Example, Divisive Analysis Clustering Solved Example, Divisive Analysis Clustering, Divisive Analysis Solved Example, Divisive Analysis Hierarchical Clustering, DIANA Hierarchical Clustering Solved Example, DIANA Hierarchical Clustering ******************************** 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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