Interview Prep
Technical topics, coding challenges, behavioral questions and system design — everything to land your AI role.
Machine Learning Fundamentals
Supervised Learning
RegressionClassificationOverfittingRegularization
- Explain bias-variance tradeoff
- What is regularization and why is it important?
- Difference between L1 and L2 regularization
- How do you handle imbalanced datasets?
Model Evaluation
Cross-validationMetricsROC-AUCPrecision-Recall
- When to use precision vs recall?
- Explain cross-validation and its types
- How to evaluate regression models?
- What is the difference between accuracy and F1-score?
Feature Engineering
ScalingEncodingFeature SelectionDimensionality Reduction
- How do you handle categorical variables?
- When to normalize vs standardize?
- Explain PCA and when to use it
- How do you detect and handle outliers?
Deep Learning
Neural Networks Basics
BackpropagationActivation FunctionsOptimizersLoss Functions
- Explain backpropagation
- Why use ReLU over sigmoid?
- What is vanishing gradient problem?
- Compare SGD, Adam, and RMSprop
CNNs
ConvolutionPoolingResNetTransfer Learning
- How does convolution work?
- Explain pooling and its types
- What are skip connections?
- When to use transfer learning?
Transformers & LLMs
AttentionSelf-AttentionBERTGPT
- Explain self-attention mechanism
- What are positional encodings?
- Difference between BERT and GPT
- How does multi-head attention work?
LLM Applications
Prompt Engineering
Few-shot learningChain-of-thoughtSystem prompts
- Best practices for prompt engineering
- How to reduce hallucinations?
- Explain few-shot prompting
- When to use chain-of-thought?
RAG (Retrieval-Augmented Generation)
Vector searchEmbeddingsChunkingReranking
- How does RAG work?
- Explain vector embeddings
- What is semantic search?
- How to improve RAG accuracy?
Fine-tuning
LoRAPEFTRLHFInstruction tuning
- When to fine-tune vs prompt engineer?
- Explain LoRA
- What is RLHF?
- How to prepare fine-tuning data?
MLOps & Production
Model Deployment
APIsContainerizationServingMonitoring
- How to deploy ML models?
- What is model serving?
- Explain A/B testing for models
- How to monitor model performance?
Scalability
Distributed trainingInference optimizationCaching
- How to scale ML systems?
- Explain data parallelism vs model parallelism
- How to optimize inference speed?
- What is model quantization?