Master Supervised Learning Algorithms: 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.
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.
🚀 Topics Covered:
• What is supervised learning?
• How Linear Regression works
• Building Decision Trees for classification and regression
• Support Vector Machines (SVM): linear vs. non-linear data
• When to use each algorithm
• Hands-on demos using real-world datasets (e.g., house prices, Iris dataset)
💻 Libraries Used:
• Python’s scikit-learn
• Pandas & NumPy for data handling
• Matplotlib & Seaborn for visualizations
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