ML Engineer with Python Test

Candidates Assessed

28712+

Organizations Served

123+

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%.

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

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

Types of Machine Learning

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

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

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

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

Topic:

Classification and Regression Algorithms - Bayesian Parameter Selection

Difficulty:

Hard

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

Topic:

Model Selection and Performance - Basic Concepts

Difficulty:

Medium

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?