Issues in ML: Bias, Underfitting & Overfitting Explained | Machine Learning From Zero | L.02

In Lecture 2, we explore the core challenges and key issues in machine learning — from model accuracy to bias-variance trade-offs. Understanding these is essential for building reliable and generalizable ML models. Topics Covered: Model Accuracy vs Precision Bias, Concept Drift, and Interpretability Overfitting & Underfitting explained Bias–Variance Trade-off with real examples 📘 Free GATE Textbook & Notes: 👉 https://gatexaiml.in 🎯 Why Watch: These concepts form the backbone of ML model evaluation and tuning — critical for GATE DA’s Machine Learning syllabus and for any real-world ML project. 📚 Watch Full Playlist: 🔹 GATE DA | Machine Learning: Supervised Playlist: https://www.youtube.com/playlist?list=PL8RhRpQueHLsfh-qDGstSB34AnXSbiGY1 #GATE #GATEDA #MachineLearning #SupervisedLearning #Accuracy #Precision #BiasVariance #Overfitting #Underfitting #ConceptDrift #DataScience #AI #GATEPreparation