🎓 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)
2. Fuzzy C Means Clustering Algorithm Solved Example Machine Learning Data Mining by Vidya Mahesh Huddar Apply Fuzzy C-Means (FCM) algorithm and find the new cluster centroids for the data is given below: Point x1 x2 x3 x4 x5 f1 1 3 5 7 9 f2 11 8 12 4 6 The number of clusters is 2, and the value of the parameter influencing the membership grade, m, is 2. The initial cluster centers are: 𝑣_1=(5, 5) 𝑎𝑛𝑑 𝑣_2 =(10, 10) What is Clustering..? Types of Clustering 1. Hard Clustering 2. Soft Clustering 1. Fuzzy C means clustering Solved example: https://youtu.be/X7co6-U4BJY 2. Fuzzy C means clustering Solved example: https://youtu.be/f3F3knVzJ0U The following concepts are discussed: ______________________________ fuzzy clustering, fuzzy c means clustering solved example, fuzzy c means clustering example, fuzzy c means clustering, fuzzy c means clustering in machine learning, fuzzy c means clusterin in data mining, c means 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