Machine Learning 08 | Random Forest & Naive Bayes | DA | GATE Crash Course

Welcome to Machine Learning Lecture 08 of the GATE DA Crash Course! In this video, we cover Random Forest and Naive Bayes, two of the most important Machine Learning algorithms frequently asked in GATE Data Analytics and Data Science interviews. You will learn the concepts with full intuition, real examples, formulas, and GATE-style numerical questions. Both algorithms are scoring, predictable, and easy to revise — making them essential for GATE DA 2025/2026 aspirants. Watch till the end to strengthen your understanding and boost your GATE score! 📌 What You Will Learn ✔ Working of Decision Trees vs Random Forest ✔ Bagging, Bootstrapping, Feature Sampling ✔ Why Random Forest reduces variance ✔ Naive Bayes Classifier (Gaussian, Multinomial, Bernoulli) ✔ Bayes Theorem & Conditional Probability ✔ Laplace Smoothing Explained ✔ GATE-style numerical examples ✔ Advantages & limitations of both algorithms #RandomForest #NaiveBayes #MachineLearning #GATEDA #GateDataAnalytics #MLAlgorithms #DataSciencePrep #GATEPreparation #MachineLearningForGATE #GATE2026 #StudyMotivation #exampreparation random forest random forest explained naive bayes naive bayes explained random forest gate da naive bayes gate da gate da random forest gate da naive bayes bagging bootstrapping decision trees ensemble machine learning crash course machine learning gate da bayes theorem gaussian naive bayes multinomial naive bayes laplace smoothing ml algorithms explained data science basics classification algorithms ml gate 2026 machine learning gate da preparation gate da important topics ensemble learning ml naive bayes interview questions