This article talks about the HackerRank enabled capabilities to assess Machine Learning Engineering skills. Machine learning engineers are responsible for creating programs and algorithms that enable machines to take actions without being directed by combining software engineering and data analysis. At HackerRank, Machine Learning Engineering skills can be assessed through our pre-designed and custom-built tests with important skills such as Python, Applied Mathematics, Statistics, PyTorch, and TensorFlow.
Note: Before we proceed, please visit here to learn more about how we define the core competencies of the skills listed above.
Using HackerRank’s Machine Learning Engineer Role, both theoretical and practical knowledge of the associated skills are tested.
Question types to Assess ML Engineering Skills
Some of the most common ways to assess Machine Learning Engineering Skills are as below:
- Hands-on Tasks (Recommended)
- Multiple Choice Questions
Real-world or Hands-on tasks and questions require candidates to dive deeper and actually demonstrate their skill proficiency. Using the hands-on questions in our library, candidates can be measured on practical demonstrations and multiple solution paths. For example, Data Science Machine Learning questions in the HackerRank library involve training a logistic regression classifier, training a Naive Bayes classifier, predicting future models of given data, among others.
Some examples of Data Science hands-on project questions in our library are as below.
Similarly, deep learning, PyTorch, and TensorFlow hands-on tasks test the understanding of fundamental concepts such as NLP, building a deep neural network, among others. Some examples of PyTorch and TensorFlow hands-on project questions in our library are as below.
Multiple-choice questions [MCQs], in general, assess conceptual knowledge and understanding of a skill. Applied Mathematics and Statistics Multiple Choice Questions, used in the Machine Learning Engineering assessments on HackerRank, test knowledge of mathematical concepts such as probability, distributions, statistical models, among others.
Some examples of Applied Mathematics and Statistics multiple-choice questions in our library are as below.
Note: Validated by our HackerRank's Skills Advisory Council, we also provide the HackerRank Skills Directory which clearly defines the competencies that can be leveraged while assessing candidates for the Machine Learning Engineering role. Please see the key competencies for the Skills listed above, on our Skills Directory here.