Everyone Explains Decision Tree Wrong | Watch This Before Learning ML | Entropy & Information Gain скачать в хорошем качестве

Everyone Explains Decision Tree Wrong | Watch This Before Learning ML | Entropy & Information Gain

🌳 Decision Tree in Machine Learning | Easy & Correct Explanation (Hindi) In this video, I explain Decision Tree in Machine Learning in a very simple, clear, and beginner-friendly way, especially for learners who feel that Decision Trees are confusing or too technical. Most tutorials focus only on formulas or library code, but in this video, the goal is to help you actually understand how a Decision Tree works internally, step by step, with intuition and real-world logic. 🧠 What is a Decision Tree in Machine Learning? A Decision Tree is a supervised machine learning algorithm used for both: Classification problems Regression problems It works by splitting data into smaller parts based on conditions, just like a tree structure: Root Node Decision Nodes Leaf Nodes Decision Trees are one of the most important algorithms in Data Science and Machine Learning because they are: Easy to interpret Powerful for real-world problems The foundation of advanced models like Random Forest and XGBoost 📚 What You Will Learn in This Video 🔹 Decision Tree Basics What is a Decision Tree? Why Decision Tree is used in Machine Learning Decision Tree for Classification vs Regression 🔹 How Decision Tree Works How data is split at each node What is a feature and condition? How decisions are made step by step 🔹 Important Concepts Explained Simply Root Node, Internal Node, Leaf Node Gini Index (basic intuition) Entropy and Information Gain (conceptual level) Overfitting in Decision Trees Why pruning is needed 🔹 Real-World Use Cases Loan approval systems Medical diagnosis Customer churn prediction Fraud detection Sales and marketing decisions 🎯 Why This Video is Different ✔ No unnecessary maths ✔ No confusing jargon ✔ Pure logic + intuition ✔ Explained in easy Hindi ✔ Perfect for beginners in Data Science and ML This video helps you build strong fundamentals, which is essential before learning: Random Forest Gradient Boosting XGBoost Advanced ML models 🎓 Who Should Watch This Video? Machine Learning beginners Data Science aspirants Engineering / BCA / MCA students Python learners moving into ML Interview preparation candidates If you want to crack ML interviews or build a career in Data Science, understanding Decision Trees is non-negotiable. 🚀 After Watching This Video, You Will Be Able To ✔ Explain Decision Tree confidently ✔ Understand splitting logic clearly ✔ Choose Decision Tree for the right problem ✔ Learn Random Forest easily ✔ Answer conceptual interview questions 📢 Support the Channel If this video helps you: 👍 Like the video 🔔 Subscribe to the channel 💬 Comment “NEXT TREE” if you want Random Forest or Decision Tree coding tutorial decision tree in machine learning, decision tree explained in hindi, decision tree data science, machine learning algorithms in hindi, decision tree classification, decision tree regression, gini index entropy information gain, data science tutorial in hindi, supervised learning in hindi, ml algorithms explained, python machine learning hindi, learn data science from scratch