These are Machine Learning questions of the kind Infosys actually asks — the patterns that keep coming back in Infosys'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.
Infosys Machine Learning concept questions
What is the difference between supervised, unsupervised and reinforcement learning? Give an example of each.
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How would you handle missing values in a dataset?
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Infosys Machine Learning applied & hands-on questions
Name the learning type and specific task for each: (1) predicting how many units of a product will sell next month, (2) grouping 50,000 product reviews with no predefined topics, (3) predicting which of 12 warehouses should fulfil an order, (4) detecting unusual patterns in server logs with no examples of 'unusual', (5) a system learning the best order to display search results from click behaviour, (6) predicting whether an employee will resign in the next 6 months.
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How to use this page: Infosys 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 Infosys Machine Learning rounds test — and the Infosys exam guide covers the rest of their selection process.