3-Minute Machine Learning - k-NN 🔑 k-nearest neighbors | Distance | Similarity | Euclidean distan...

[3-Minute Machine Learning] Starting with an overview of machine learning, this course concisely introduces 29 key machine learning algorithms, both supervised and unsupervised, focusing on core ideas. Supervised learning algorithms include the following machine learning models: linear regression, logistic regression, penalized regression (Ridge, Lasso, ElasticNet), multivariate adaptive regression splines (MARS), generalized additive models (GAM), local regression (LOESS), k-NN, naive Bayes, support vector machines (SVM), decision trees, bagging, random forests, AdaBoost, GBM, XGBoost, stacking, artificial neural networks, and deep learning. Unsupervised learning algorithms include the following machine learning models: principal component analysis (PCA), t-SNE, UMAP, k-means clustering, hierarchical clustering, DBSCAN, OPTICS, GMM, self-organizing maps (SOM), market basket analysis (association rule analysis), and recommender systems. 📺 Go to the 『Kwak Ki-young』 channel:    / kwak   Ki-young 📚 The video lectures on the 『Kwak Ki-young』 channel are based on the following books: 💕 『R Basics and Applications』 (Kwak Ki-young, Chungram Publishing) 『Statistical Data Analysis with R』 (Kwak Ki-young, Chungram Publishing) 『Machine Learning and Text Mining with R』 (Kwak Ki-young, Chungram Publishing) 『Web Scraping and Data Analysis with R』 (Kwak Ki-young, Chungram Publishing) 『Statistical Data Analysis with SPSS』 (Kwak Ki-young, Chungram Publishing) 『Social Network Analysis』 (Kwak Ki-young, Chungram Publishing) #MachineLearning #DataAnalysis #Statistics #DataAnalytics #DataScience #RProgramming