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AI Engineer Interview Questions for Fresher

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  This collection of 20  AI Engineer interview questions and answers for freshers  covers fundamental concepts in AI, machine learning, deep learning, and NLP. Designed for beginners, it explains key topics like supervised vs. unsupervised learning, overfitting, data preprocessing, reinforcement learning, and popular AI tools, helping freshers build confidence for entry-level AI interviews. Que 1. What is the difference between Artificial Intelligence, Machine Learning, and Deep Learning? Answer: AI: Broad field of making machines mimic human intelligence. ML: Subset of AI where machines learn from data. Deep Learning: Subset of ML using neural networks with many layers. Que 2. What are some real-world applications of AI? Answer: Chatbots and virtual assistants. Recommendation engines (Netflix, Amazon). Fraud detection in banking. Healthcare (disease diagnosis). Autonomous vehicles. Que 3. What is supervised learning? Give an example. Answer: Supervised learning uses labe...
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AI Engineer Interview Questions for Experienced

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  This guide provides 20 carefully structured AI Engineer interview questions and answers , starting from fundamental machine learning concepts to advanced topics like deep learning, reinforcement learning, attention mechanisms, and ethical AI practices. It is designed to help candidates prepare for real-world AI engineering interviews with practical explanations, examples, and insights. Que 1. What is the difference between supervised, unsupervised, and reinforcement learning? Answer: Supervised Learning: Trains models on labeled data (e.g., predicting house prices). Unsupervised Learning: Finds hidden patterns in unlabeled data (e.g., clustering customers). Reinforcement Learning: Learns by interacting with an environment using rewards and penalties (e.g., game AI). Que 2. How do you handle imbalanced datasets in machine learning? Answer: Resampling: Oversampling minority class or undersampling majority class. Synthetic Data: Use SMOTE (Synthetic Minority Oversampling Techni...
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