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ML Engineer with Python Test
Test duration:
60
min
No. of questions:
22
Level of experience:
Entry/Mid/Expert

ML Engineer with Python Test

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

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ML Engineer with Python Test

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

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

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How it works

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, 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 and quick way.

Useful for hiring
  • Data Scientists
  • Data Science Engineer
  • Data Science Developer
  • Data Science Associate
  • Data Analyst
  • Machine Learning Engineer
Test Duration
60
min
No. of Questions
22
Level of Expertise
Entry/Mid/Expert
Topics Covered
Shuffle

Classification and Regression Algorithms

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

Bootstrapping

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

Types of Machine Learning

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

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
Shuffle

Scikit-learn

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
Shuffle

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 Question
Choose from our 100,000+ questions library or add your own questions to make powerful custom tests.
Question type
Multiple Option
Topics covered
Classification and Regression Algorithms - Bayesian Parameter Selection
Difficulty
Hard

Question:

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.
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Test Report
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

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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.
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