Top 10 AI and ML interview questions You Should Prepare For!

What is the difference between AI, ML, and DL?
AI is the broad field, ML is machines learning from data, DL is ML using deep neural networks.

Explain supervised vs unsupervised learning with examples.
Supervised uses labeled data (spam detection), unsupervised finds patterns (customer segmentation).

What is overfitting and how to prevent it?
Overfitting means the model is too tailored to training data; use techniques like cross-validation, pruning, or regularization.

Describe precision, recall, and accuracy. When to prioritize each?
Accuracy = overall correctness, precision = correct positives, recall = capturing all positives. E.g., recall is key in disease detection.

What are common loss functions in ML?
Mean Squared Error for regression, Cross-Entropy for classification.

How does gradient descent work?
Optimizer adjusts model weights to minimize loss by following the gradient.

What is the curse of dimensionality?
Too many features can lead to sparse data and poor model performance.

Explain bias-variance tradeoff.
Bias = error from oversimplification, variance = error from too complex model; balance needed.

What are LSTM networks used for?
Handling sequential data like time series or language.

Difference between generative and discriminative models?
Generative models create new data, discriminative models classify existing data.

• Column Ordering
• Overflow Handling in CSS
• PHP Error Handling
• Soft Skills for Developers
• Perplexity Computer: The AI-First Future of Personal Computing

For more free projects, check our Bootstrap Templates section.
Visit → https://codevigyaan.com/bootstrap-projects

Want HTML & CSS projects?
Open → https://codevigyaan.com/projects/

For more free study material and handwritten notes?
Open → https://codevigyaan.com/free-e-books/