AI/ML Interview Tests

ML Fundamentals
Core ML algorithms, model selection, and supervised/unsupervised learning fundamentals.
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Deep Learning
Neural network architectures, training dynamics, and modern deep learning techniques.
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NLP & LLMs
Natural language processing, transformer models, and large language model concepts.
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Computer Vision
Image recognition, CNNs, object detection, and vision model architectures.
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Reinforcement Learning
Reward-based learning, Q-learning, policy gradients, and RL applications.
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Statistics & Probability
Statistical inference, probability theory, hypothesis testing, and distributions.
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Data Preprocessing
Feature engineering, normalization, handling missing data, and data pipelines.
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Model Evaluation
Metrics, cross-validation, confusion matrices, and model performance assessment.
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MLOps & Deployment
Model serving, monitoring, CI/CD for ML, and production deployment patterns.
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AI Ethics
Fairness, bias, accountability, transparency, and societal implications of AI.
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Generative AI
Diffusion models, GANs, fine-tuning, RLHF, and generative system techniques.
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AI Agents
Agentic architectures, tool use, memory, planning, and multi-agent coordination.
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