Data Science Interview Prep | Machine Learning Foundations Explained in 6 Minutes | Part 1

Crash course that covers the most asked ML foundations questions you need to know

👉 What you’ll learn in this video:
✅ Difference between AI, Machine Learning, and Deep Learning
✅ Supervised, Unsupervised, and Reinforcement Learning (with examples)
✅ Overfitting vs Underfitting & how to prevent them
✅ Why we split Training, Validation, and Test datasets
✅ Bias-Variance Tradeoff explained simply
✅ What is Cross-Validation and why it matters
✅ Activation Functions in Neural Networks
✅ Gradient Descent & the Vanishing Gradient Problem (and how ReLU solves it)
✅ Batch vs Stochastic vs Mini-batch Gradient Descent
✅ Classification vs Regression in ML
✅ What is Regularization (L1 vs L2) and why it prevents overfitting

🎓 Perfect for students, AI enthusiasts, and anyone curious about how machines understand human language.

🔗 Keep learning:
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📺 [Attention is All You Need Playlist] ▶️ [   • Attention is All You need | Crash Courses  ]
📺 [Data Science Career Guide] ▶️ [   • Career Guidance in Data Science  ]
📺 [Artificial Intelligence Building Blocks] ▶️ [   • Artificial Intelligence Building Blocks  ]

🌍 Animated learning from Africa to the world — Data Science Animated by Lubula.

🔗Krish Naik:
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