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Introduction to ML Concepts Interviews

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The ML concept interview typically lasts about 45 minutes, and it assesses your understanding of fundamental ML concepts. Depending on the company, the interview may have a different title (e.g. ML technical assessment, ML theory, ML knowledge).

What to expect

This interview is conducted for candidates at all levels (junior, mid-level, and senior) and follows a rapid-fire Q&A format led by the interviewer. The question's level of difficulty increases with the candidate's experience.

The scope of ML is large, so there are countless questions the interviewer could ask. To ensure thorough coverage of potential ML concept questions, we’ve identified four major categories:

  1. Data handling: e.g. "What are some common transformations for categorical data?"
  2. Model selection and optimization: e.g. "Describe how splits in a decision tree occur."
  3. Evaluation methods and metrics: e.g. "Explain when accuracy would be a good metric and a bad metric to measure how well your model is performing."
  4. ML in production: e.g. "How do you know when it is time to replace an ML model in production?"

Revise smarter, not harder. Jump here for the essential ML models and algorithms you need for interviews.

What interviewers look for

In the ML Concepts interview, you’re assessed on how well you:

  • Understand the technical details of fundamental ML concepts.
  • Demonstrate your experience modeling unwieldy datasets that mimic real-world scenarios, particularly those relevant to the team you’re interviewing for.
  • Communicate your responses with clarity and confidence. This seems obvious, but it’s common for candidates to overthink questions and freeze up.

How to prepare

During your interview prep, try to cover the following:

  1. Begin by learning more about the team you’re interviewing for. In particular, look at what recent products and features the team has released. Read any papers and blog posts they’ve published.
  2. Skim through some top papers in their particular sub-field to learn about the current state-of-the-art models.
  3. Ask the company if they can provide the interviewer's name. Before the interview, research their professional background and areas of expertise to gauge what they might interview you on.

Lastly, check out the following resources to gain the high-level and implementation knowledge:

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