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Random Forest (Ensembling Technique)

Mastering Ensemble Learning: Bagging & Boosting Explained! Welcome to today's workshop on Ensemble Learning! 🚀 In this session, we will explore how combining multiple models improves accuracy and performance. 📌 Timestamps: ⏳ 0:00 - Introduction to Ensemble Learning ⏳ 0:20 - What is Ensemble Learning? ⏳ 0:42 - Two Main Techniques: Bagging & Boosting ⏳ 0:52 - What is Bagging (Bootstrap Aggregation)? ⏳ 1:10 - How Bagging Works: Training Models in Parallel ⏳ 1:36 - Why Bagging Helps Reduce Variance & Overfitting ⏳ 2:00 - Random Forest: How It Uses Bagging ⏳ 3:00 - How Random Forest Works (Step-by-Step Explanation) ⏳ 5:00 - Example: Decision Trees & Majority Voting in Random Forest ⏳ 7:00 - Boosting Explained: Training Models Sequentially ⏳ 8:00 - How Boosting Corrects Previous Model Mistakes ⏳ 9:00 - Examples of Boosting: XGBoost, AdaBoost ⏳ 10:00 - Comparing Bagging vs. Boosting ⏳ 11:00 - Summary & Final Thoughts 📢 Check out our other AI & ML videos: 🔹    • Supervise Machine Learning , Regression an...   🔹    • Machine Learning Life Cycle updated   🔹    • Logistic Regression Part 1   🔹    • Logistic Regression Part 2 Math Foundation   🔹    • IRIS Flower class prediction model manual ...   📌 Follow Us on Social Media for More ML & AI Content: 🔹 Instagram:   / deepneuron.ai   🔹 Twitter (X): https://x.com/DeepneuronA 🔹 Website: https://deepneuron.in/ 🔹 Website: https://training.deepneuron.in/ 📲 Call/WhatsApp for AI & ML Training: +91 90432 35205 💬 Drop a comment with your questions and experiences using Docker! Don't forget to LIKE 👍 and SUBSCRIBE 🔔 #MachineLearning #AI #EnsembleLearning #Bagging #Boosting #RandomForest #MLAlgorithms #DataScience #ArtificialIntelligence #TechEducation #MachineLearning #EnsembleLearning #Bagging #Boosting #RandomForest #XGBoost #AdaBoost #AI