Unlock the power of supervised learning with this in-depth guide to three of the most essential machine learning algorithms: Linear Regression, Decision Trees, and Support Vector Machines (SVM). In this video, you'll learn how each algorithm works, when to use them, and see practical demos using real-world datasets. Perfect for data science beginners and professionals alike, this tutorial walks through the basics, provides coding examples, and explains key concepts in a simple, engaging way.br Whether you're predicting house prices or classifying data, you'll leave with a solid understanding of these powerful algorithms and when to apply them in your projects.br Topics Covered:br • What is supervised learning?br • How Linear Regression worksbr • Building Decision Trees for classification and regressionbr • Support Vector Machines (SVM): linear vs. non-linear databr • When to use each algorithmbr • Hands-on demos using real-world datasets (e.g.