# Best way to prepare for AI/ML interviews?

**URL:** <https://forum.guvi.in/t/best-way-to-prepare-for-ai-ml-interviews/359>\
**Category:** General\
**Tags:** ai-ml\
**Created:** [January 23, 2026, 1:09pm UTC](https://forum.guvi.in/t/best-way-to-prepare-for-ai-ml-interviews/359 "2026-01-23T13:09:48Z")\
**Posts on this page:** 2\
**Page:** 1

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**Author:** ![narankarthi101113457](https://avatars.discourse-cdn.com/v4/letter/n/f0a364/32.png) [@narankarthi101113457](https://forum.guvi.in/u/narankarthi101113457)\
**Post date:** [January 23, 2026, 1:09pm UTC](https://forum.guvi.in/t/best-way-to-prepare-for-ai-ml-interviews/359/1 "2026-01-23T13:09:48Z")

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What do AI/ML interviewers actually look for in candidates? How should one prepare beyond theory?

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**Author:** ![astha37422](https://avatars.discourse-cdn.com/v4/letter/a/8edcca/32.png) [@astha37422](https://forum.guvi.in/u/astha37422)\
**Post date:** [January 23, 2026, 1:27pm UTC](https://forum.guvi.in/t/best-way-to-prepare-for-ai-ml-interviews/359/2 "2026-01-23T13:27:58Z")

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From what I’ve seen, the **best way to prepare for AI/ML interviews** is to balance **fundamentals, hands-on practice, and clear explanation** , rather than just memorizing algorithms.

Start with the **basics** : Python, statistics, probability, linear algebra (at a conceptual level), and how common ML algorithms work. Interviewers often care more about _why_ you’d choose a model and _how_ it behaves than the exact math formulas.

Next, focus on **practical ML skill** data cleaning, feature engineering, model evaluation, and handling real-world issues like overfitting or imbalanced data. Be ready to walk through **your projects end to end** : problem statement, data approach, model choice, results, and what you’d improve.

It also helps to practice **coding and ML questions** on platforms like LeetCode or Kaggle, but don’t overdo it. Many interviews test your ability to **think aloud and reason through problems**.

If you’re new and want a structured path, some candidates use guided programs or courses to cover fundamentals and projects in a systematic way. That can help with confidence, but interview success still comes down to how well you understand and explain your work.

In short: **strong basics + real projects + clear communication** is what usually makes the difference in AI/ML interviews.