๐ Complete Machine Learning Playlist | Algorithms & Solved Examples Welcome to the Ultimate Machine Learning Playlist โ 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. --- ๐ Algorithms & Topics Covered: ๐ธ Supervised Learning ๐ Decision Trees (ID3, Gini Index, C4.5, CART, Boolean Function Trees) ๐ Naive Bayes Classifier (Laplace Correction, Text Classification) ๐ K-Nearest Neighbors (KNN with various distance metrics) ๐ Linear & Logistic Regression (Simple, Multiple, Matrix Form) ๐ Support Vector Machines (SVM, Linear/Non-Linear, Kernel Methods) ๐ Neural Networks (Perceptron, Backpropagation, MLP, RBF, Activation Functions) ๐ธ Unsupervised Learning ๐ K-Means Clustering (Standard, K-Means++, Bisecting K-Means) ๐ K-Medoids Clustering ๐ Hierarchical Clustering (Single, Average, Complete Linkage) ๐ DBSCAN, EM Algorithm, DIANA Clustering ๐ Dimensionality Reduction (PCA, SVD, KL Transform) ๐ธ Ensemble & Advanced Methods ๐ฒ Random Forest ๐ก Genetic Algorithms (Selection, Crossover, Mutation) ๐ Bayesian Belief Networks ๐ Gradient Descent (Batch, Stochastic, Delta Rule) ๐งช Feature Selection (Wrapper, Filter, Embedded) ๐ธ Other Key Concepts ๐ง Overfitting & Regularization (Lasso, Ridge, Elastic Net) ๐ฏ ROC Curve, Confusion Matrix, Accuracy, Precision, Recall, F1 Score ๐ Distance Measures (Euclidean, Manhattan, Minkowski, Cosine, Hamming) ๐ Outlier Detection (IQR, Z-Score, SMOTE, NearMiss, Tomek Links) ๐งพ Association Rules (Apriori, ECLAT) ๐งฎ Case-Based Reasoning, Candidate Elimination, Find-S ๐ฌ TF-IDF, Sentiment Analysis, One-Hot Encoding --- ๐ 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 ๐ Subscribe and turn on notifications to stay updated!
Curated by: Mahesh Huddar (337 videos)
10. KNN Algorithm Solved Example Classify Maths=6, CS=8 Using K=3 Machine Learning by Vidya Mahesh Huddar Use KNN algorithm to classify the instance: ๐= (๐๐๐กโ๐ =6, ๐ถ๐=8) Into Pass or Fail Given ๐พ=3. for the dataset Training Dataset: Maths CS Result ----- -- ------ 4 3 Fail 6 7 Pass 7 8 Pass 5 5 Fail 8 8 Pass Test Instance: (Maths = 6, CS = 8) K = 3 Objective: Use the K-Nearest Neighbors (KNN) Algorithm to classify the given test instance into either Pass or Fail using Euclidean Distance and Majority Voting. K nearest Neighbor Learning Algorithm 1: https://youtu.be/w6hQ3ES6Kbg K nearest Neighbor Learning Algorithm 2: https://youtu.be/UAIIW3Bs5HE KNN example 1: https://youtu.be/Vk9lGGODaJA KNN example 2: https://youtu.be/HZT0lxD5h6k KNN example 3: https://youtu.be/T0YkfWssHjk KNN example 4: https://youtu.be/kCNpLbCUo7g KNN example 5: https://youtu.be/WgSGdukowNA KNN example 6: https://youtu.be/gCZsLAzHlEM KNN example 7: https://youtu.be/-OqKQS8KAe8 KNN example 8: https://youtu.be/C75Arp5mg6I KNN example 9: https://youtu.be/zkI2i7RSMf4 KNN example 10: https://youtu.be/jsUbTOcV8pU The following concepts are discussed: ______________________________ k nearest neighbors algorithm, knn algorithm, knn algorithm in machine learning, k nearest neighbors,knn classifier, knn classification, knn solved example ******************************** 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