Lecture 11: Decision Tree Algorithm Explained 🌳 | Machine Learning Theory for Beginners

🌳 Lecture 11 – Decision Tree Algorithm | Machine Learning Series How does a machine learning model make decisions just like a flowchart? In this lecture, we'll understand the Decision Tree algorithm with simple explanations and examples! 📚 What you'll learn in this video: ✅ What is a Decision Tree in Machine Learning? ✅ How Decision Trees work ✅ Root nodes, internal nodes, branches, and leaf nodes ✅ Splitting criteria: Gini Impurity and Entropy ✅ Information Gain and how the best split is selected ✅ How a Decision Tree makes predictions ✅ Overfitting in Decision Trees and the importance of pruning ✅ Classification vs. Regression Trees ✅ Real-world applications of Decision Trees 🎯 Who is this video for? Beginners learning Machine Learning, Python, and Data Science who want to understand algorithms from the basics. 🌱 Next Lecture: Lecture 11.1 – Decision Tree Practical in Python, where we'll train a model, make predictions, evaluate accuracy, and visualize the tree using Scikit-learn. If you find this lecture helpful, don't forget to LIKE, SHARE, and SUBSCRIBE for more beginner-friendly Machine Learning tutorials! #MachineLearning #DecisionTree #DataScience #Python #MachineLearningForBeginners #ArtificialIntelligence #DecisionTreeAlgorithm