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Mathematics For Machine Learning Skills Test
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No. of questions:
Level of experience:
Entry Level/Mid/Senior

Mathematics For Machine Learning Skills Test

This skills test allows you to evaluate individuals' abilities faster and conduct skills benchmarking. It can also help talent managers get an objective evaluation, bridge skill gaps, and reduce technical screening time by 80%.

A scientific calculator, a wooden ruler, a graphite pencil, and a handwritten mathematical formula on lined paper
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Mathematics For Machine Learning Skills Test

It is the foundation of machine learning's four main ideas: linear algebra, probability theory, calculus, and statistics. It helps design, debug, and apply various algorithms in machine learning and helps choose the right parameters and find insights.  

Why Use iMocha's Mathematics for Machine Learning Skills Test?

iMocha's test includes questions on concepts like Bayes' theorem, hypotheses testing, regression analysis, dot product, vector and matrix, and more. Its proctoring capabilities eliminate the scope of cheating. Utilize it to evaluate your employees' and candidates' strengths and weaknesses with a data-driven approach.  

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How it works

Test Summary

This skills test can assess individuals for the following traits:  

  • Knowledge of different mathematical concepts work, including the following:  
  1. Statistics and probability (conditional probability, hypothesis testing, regression analysis, etc.)  
  1. Linear algebra (matrix transpose and inverse, dot product, etc.)  
  1. Calculus (Step, Sigmoid, Logit, ReLU, Maxima, Minima, etc.)  
  • Understanding how algorithms and optimization technique's function, familiarity with cost and likelihood functions, Bias-Variance tradeoffs, etc.  
  • Ability to pay attention to detail and efficiently learn new concepts in the evolving ML industry.  
  • Analytical thinking and problem-solving abilities.  

It also offers detailed results of your candidates and employees, helping you identify their suitability for a role. Moreover, all the questions are created as per EEOC compliance to ensure unbias talent decisions.  

Useful for hiring
  • Machine Learning Engineer
  • Data Scientist
  • Algorithm Developer
  • Quantitative Analyst
  • Research Scientist
  • Statistician
Test Duration
No. of Questions
Level of Expertise
Entry Level/Mid/Senior
Topics Covered





Sample Question
Choose from our 100,000+ questions library or add your own questions to make powerful custom tests.
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Topics covered


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You can customize this test by

Setting the difficulty level of the test

Choose easy, medium, or tricky questions from our skill libraries to assess candidates of different experience levels.

Combining multiple skills into one test

Add multiple skills in a single test to create an effective assessment and assess multiple skills together.

Adding your own
questions to the test

Add, edit, or bulk upload your coding, MCQ, and whiteboard questions.

Requesting a tailor-made test

Receive a tailored assessment created by our subject matter experts to ensure adequate screening.
How is this skill test customized?
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The skills test can be customized to include your own questions related to specific technical or non-technical skills. It can contain questions on topics like the Naive Bayes model, time management, communication functions, matrix transposition, etc. Likewise, questions can be customized based on candidates' and employees' difficulty levels and experience.

What are the most common interview questions related to Mathematics for Machine Learning?
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Here are some commonly asked interview questions for this field:

  • What is the Naive Bayes model?  
  • Explain the popular activation functions.  
  • What is the confusion matrix?  
  • Where can you use linear algebra concepts in machine learning?  
  • What is Principal Component Analysis (PCA)?  
  • What is the Bias-Variance tradeoff?  
  • How would you use discreet mathematics in machine learning?  
  • Should you design robust or accurate algorithms?  
  • What is cross-validation?  
  • What is overfitting? And how can you avoid it?  
  • Is a mean square error a bad measure of model performance?  

Need help creating a custom set of questions? iMocha can help!  

What are the required skillsets to work on maths in ML?
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Here is a list of common technical and non-technical skills required from the individuals:  

Soft skills:  

  • Analytical thinking  
  • Problem-solving  
  • Having a learning mindset  
  • Paying attention to detail, etc.  

Technical skills:  

  • Linear algebra  
  • Calculus  
  • Statistics  
  • Probability