📘 Lesson 04 – Supervised Learning | AI & Machine Learning Basics | TechLearn

📘 Lecture 04 – Supervised Learning | AI & Machine Learning Basics | TechLearn In this lecture, we dive into Supervised Learning — one of the core concepts in Machine Learning — covering the key algorithms every ML learner must know, with simple explanations, real-life analogies, and worked examples. 🔍 Topics covered in this video: 00:00 – Introduction to Supervised Learning 📈 Linear Regression – Simple & Multiple, OLS, R² (with worked example) 📊 Logistic Regression – Sigmoid Function, Logit, Prediction Cutoff (with worked example) 📍 K-Nearest Neighbors (KNN) – Euclidean Distance, Classification & Regression 🌳 Decision Trees (CART) – Root/Internal/Leaf Nodes, Gini Index, Splitting. 🧮 Support Vector Machines (SVM) – Margin, Soft Margin. ✅ Quick Recap – Comparing all 5 algorithms side by side This lecture is part of the AI & Machine Learning Basics playlist, designed to build a strong foundation in machine learning concepts through practical, easy-to-follow examples. 📚 Previous lectures in this series: Lecture 01 – Introduction to AI & Machine Learning Lecture 02 – What is Expert Systems and Ontologies Lecture 03 - Machine Learning Pipeline(Part 1, part 2, part 3) 👍 If you find this helpful, like, subscribe, and turn on notifications for more ICT and AI/ML learning content! #MachineLearning #SupervisedLearning #AI #TechLearn #DecisionTrees #SVM #LogisticRegression #LinearRegression #KNN #DataScience