Master Supervised Learning: Linear Regression, Decision Trees & SVM Explained! 🚀

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 works<br />• Building Decision Trees for classification and regression<br />• Support Vector Machines (SVM): linear vs. non-linear data<br />• When to use each algorithm<br />• Hands-on demos using real-world datasets (e.g., house prices, Iris dataset)<br /> Libraries Used:<br />• Python’s scikit-learn<br />• Pandas & NumPy for data handling<br />• Matplotlib & Seaborn for visualizations<br />