These are Machine Learning questions of the kind TCS actually asks — the patterns that keep coming back in TCS's AI project interviews, where what matters is applying Machine Learning sensibly for clients rather than research depth. Treat this page as a mock interview: say every answer out loud before revealing it. If one surprises you, the lesson behind it is linked at the bottom.
TCS Machine Learning concept questions
What is machine learning, and how is it different from traditional programming?
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What is overfitting, how would you detect it, and how would you fix it?
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TCS Machine Learning applied & hands-on questions
You built a fraud model with 98% accuracy. The client is delighted. Write what you would actually say in the meeting.
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How to use this page: TCS rarely asks something you've never seen — they ask a standard Machine Learning concept and then push one level deeper ("why?", "what would you do if..."). Master the concept in the Machine Learning course lessons, and the follow-up stops being scary.
Keep practising: Supervised vs Unsupervised, Overfitting & Underfitting, Evaluation Metrics and ML Project Lifecycle cover what most TCS Machine Learning rounds test — and the TCS NQT exam guide covers the rest of their selection process.