KNN & Decision Tree | Learn AI from Scratch in 50 days | AI & ML Explained Simply | day 4 скачать в хорошем качестве

KNN & Decision Tree | Learn AI from Scratch in 50 days | AI & ML Explained Simply | day 4

In this video, we explore two fundamental machine learning classification algorithms: k-Nearest Neighbor (kNN) and Decision Trees. You’ll learn how kNN classifies data based on similarity and distance metrics, how to handle tie situations, and where kNN is best used in real-world applications like recommendation systems and document search. We also dive into Decision Trees, understanding how they make decisions using simple logical rules and tree structures. Through hands-on Python examples using Scikit-learn, we demonstrate: How classification works with 2D data points How to train and test Decision Tree models How to evaluate models using a confusion matrix How accuracy is calculated in machine learning This video is perfect for beginners in Machine Learning and AI, students, and anyone preparing for interviews or building a strong foundation in classification algorithms. 🎯 Topics Covered: k-Nearest Neighbor (kNN) Algorithm Distance Metrics & Similarity Handling Ties in kNN Decision Trees Explained Scikit-learn Implementation Confusion Matrix & Accuracy 🔖 Hashtags #MachineLearning #ArtificialIntelligence #DataScience #kNN #KNearestNeighbor #DecisionTrees #ClassificationAlgorithms #ScikitLearn #PythonForML #AIForBeginners #MLTutorial #DataScienceTutorial #LearnMachineLearning #AIEducation