Entire Data Science Interview Prep | From Foundations to Advanced & Tricky Concepts | Part 1-3

Crash course covering the most common Machine Learning foundation questions you need to know. Dive deep into advanced and tricky Machine Learning concepts with this course. Explore intermediate Machine Learning concepts in depth with this comprehensive crash course.

👉 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
✅ What is the vanishing gradient problem? Why is it an issue, and how was it solved by ReLU?
✅ What is batch normalization and why does it help training?
✅Regularization technique specific to neural networks. the differences
between L1 and L2 regularization
✅ What is dropout In deep learning
✅What are evaluation metrics ( precision, recall,, ROC-AUC) and when would you use each?
✅ What is k-fold Cross-Validation and why it matters
✅ What is Dimensionality Reduction and PCA (Principal Component Analysis)? Why is it used?
✅ What is the difference between generative and discriminative models?
✅Complex generative model architecture and its difficulties
✅What is double descent phenomenon in modern deep learning, and how does it
✅ What is the difference between max-margin loss (SVM) and cross-entropy loss? When would each be preferred and why?
✅Why does batch normalization important for transformer architecture
✅ Explain attention mechanisms and how they led to Transformers
✅ Catastrophic forgetting👉 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
✅ What is the vanishing gradient problem? Why is it an issue, and how was it solved by ReLU?
✅ What is batch normalization and why does it help training?
✅Regularization technique specific to neural networks. the differences
between L1 and L2 regularization
✅ What is dropout In deep learning
✅What are evaluation metrics ( precision, recall,, ROC-AUC) and when would you use each?
✅ What is k-fold Cross-Validation and why it matters
✅ What is Dimensionality Reduction and PCA (Principal Component Analysis)? Why is it used?
✅ What is the difference between generative and discriminative models?
✅Complex generative model architecture and its difficulties
✅What is double descent phenomenon in modern deep learning, and how does it
✅ What is the difference between max-margin loss (SVM) and cross-entropy loss? When would each be preferred and why?
✅Why does batch normalization important for transformer architecture
✅ Explain attention mechanisms and how they led to Transformers
✅ Catastrophic forgetting

🎓 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  ]

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