ML Engineer with Python Test

Candidates Assessed


Organizations Served


iMocha's Machine Learning coding test enables recruiters and hiring managers to assess Python programming skills for Machine Learning. Machine test for python can be useful in hiring Data Scientists, Data Science engineers, Data Science developers, Data Science associates, Data Analysts, and Machine Learning engineers. Our test can help you reduce hiring cost by 40%.

About ML Engineer with Python Test

Machine learning with Python is a type of data science technique in which programmers work with machine learning algorithms to put data into effective work. Machine Learning with Python mainly focuses on the development of various computer programs that can help the programmer to change it when exposed to new data.

The machine learning coding test helps recruiters & hiring managers to assess candidates’ Python programming skills for machine learning. Machine test for Python is designed by experienced Subject Matter Experts (SME) to evaluate and hire machine learning engineer as per the industry standards.

Are you a jobseeker looking to sharpen your skills?

Test Summary

Machine learning coding test helps to screen the candidates who possess traits as follows: 
  • Ability to write code with Python libraries that support machine learning algorithms
  • Knowledge of supervised learning, unsupervised learning, and reinforcement learning
  • Understanding concepts like NumPy with Python, machine learning with SciKit Learn, linear regression, K Nearest Neighbors, K Means clustering, decision trees, random forests, support vector machines
  • Excellent experience in Natural Language Processing, Neural Networks, and Deep Learning
Machine test for Python for machine learning with python contains a coding simulator that will automatically evaluate and provide a score for the candidate’s written codes by compiling multiple test cases that generate discrete output. You will also get a detailed report for each test case execution along with execution time and execution memory usage for the program written by the candidate. The Code-Replay feature records the coding screen of the candidate so that the reviewer can understand the coding and thinking patterns of the candidate.

This Machine Learning coding test may contain coding questions and innovative AI-LogicBox (pseudo coding platform) questions to assess a candidate's coding skills in a fun & quick way.

Test Duration: 60 minutes

No. of Questions: 22

Level of Expertise: Entry/Mid/Expert

Useful for hiring

  • Data Scientists
  • Data Science Engineer
  • Data Science Developer
  • Data Science Associate
  • Data Analyst
  • Machine Learning Engineer

Topics Covered

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Classification and Regression Algorithms

The machine learning coding test evaluates the candidate's understanding and the use of  Classification and Regression Algorithms

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Machine test for Python evaluates candidate's knowledge of Bootstrapping to avoid overfitting and improves the stability of the machine learning algorithm

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Types of Machine Learning

The test contains questions on types of Machine learning to measure candidate's in-depth understanding of ML

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Data Cleaning

Machine Learning coding test assesses a candidate's knowledge of preparing data for analysis by removing or modifying data that is incorrect, incomplete, irrelevant, duplicated, or improperly formatted

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The tech assessment evaluates candidates based on their knowledge of the Scikit-learn library which contains a lot of efficient tools for machine learning and statistical modeling including classification, regression, clustering, and dimensionality reduction

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Machine Learning Coding

The Machine test for Python gauges a candidate's knowledge on interdisciplinary field of research related to Natural Language Processing, Programming Language Structure, and Social and History analysis such contributions graphs and commit time series

Sample Questions

Choose from our 100,000+ question library or add your own questions to make powerful custom tests

Question types:

Multiple Option


Classification and Regression Algorithms - Bayesian Parameter Selection



Q 1. In which of the following conditions will the maximum a-posteriori hypothesis be equal to the maximum likelihood estimate hypothesis?
When the parameter θ has a lognormal distribution.
When the parameter θ has an exponential distribution.
When the parameter θ has a uniform distribution.
None of the options.

Question types:

Multiple Option


Model Selection and Performance - Basic Concepts



Q 2. During a meeting, the team lead asks you about the set of options that can be used as metrics for model performance. As a machine learning expert, you list down the set of performance metrics from your past experience. Which set of metrics from the given options will you choose?
Confusion Matrix
All of the options

Sample Report

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Skill wise performance report by iMocha

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You can customize this test by

difficulty level
Setting difficulty level of test      

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

multiple skills
Combining multiple skills into one test

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

adding own skill
Adding your own questions to the test

Add, edit or bulk upload your own coding questions, MCQ, whiteboarding questions & more.                       

tailor made test
Requesting a tailor-made test                  

Get a tailored assessment created with the help of our subject matter experts to ensure effective screening.

Trusted By

Christoph, e-Zest GmbH
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“Our recruitment team loves iMocha especially for their skill assessments, simulators, and friendly support.”

Christoph, e-Zest GmbH

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