Hire Machine Learning Engineers with experience in Python

Machine Learning Engineers Assessment
Machine learning is a subset of AI that grants systems the ability to learn new things on their own and evolve from experiences without explicitly being programmed. Machine learning is intended to develop computer programs that have data access and use it to learn for themselves. The learning process starts with data observations, such as direct experience or commands to establish new patterns in data and make better decisions in the future. The main objective is to minimize human intervention as much as possible and let the computers learn, assist, and optimize actions accordingly.

Machine Learning assessment test enables recruiters and hiring managers to find the most appropriate candidates by assessing their capabilities and expertise in machine learning. Experienced subject matter experts play a crucial role in the design and validation of these assessments. This is precisely why the process of evaluating the expertise of ML professionals becomes effective with such assessments. Employers can use this machine learning assessment to screen those candidates who demonstrate skills as follows:

  • Proficiency in machine learning algorithms(k-NN, SVM, etc.) and techniques
  • Well-versed in exploratory data analysis
  • Skilled in preparing and analyzing data and  recognizing patterns
  • Excellent understanding of  linear regression, LDA

Machine learning assessment is useful in a comprehensive evaluation of candidates’ practical knowledge and helps recruiters identify whether the test takers are job-ready or not. Nowadays, it is necessary to emphasize the knowledge of applied skills acquired through professional experience, rather than theoretical wisdom. For this reason, the machine learning assessment will hold relevance for recruiters and hiring managers.

Why is Python important for Machine Learning?
Python is one of the most popular programming languages in the world. It is used for a variety of purposes ranging from web development to process automation and scripting. Favored for its multipurpose use, Python is steadily rising to fame among machine learning developers over recent years. It is an essential programming language for developers working in the fields of machine learning. Developers consider Python for its plethora of applications, so given below are reasons why this language is ideal for machine learning professionals: 

Vast selection of libraries and frameworks
Python is abundant with libraries and frameworks that make coding effortless and save development time for machine learning engineers. These Python libraries and frameworks are extensively used in machine learning:

  • Scipy
  • Numpy
  • Theano
  • Scikit-learn
  • TensorFlow
  • Keras
  • Pandas
  • PyTorch
  • Matplotlib

Ease of use
The language is known for its short, readable code, and is easy to use for new developers. This feature comes in handy for machine learning (ML) professionals. Although ML depends on complex algorithms and multi-stage workflows, it’ll become easier for developers, if the coding is not complex, to focus on problem-solving and completing projects in time. Python’s simple syntax enables developers to check algorithms instantly without having an obligation to implement them. An easily readable code also enables ML developers for collaborative coding. This is true when a project entails third party components or ample custom business logic  

Python’s wide selection of machine learning-centric libraries and frameworks speeds up the development process. It’s easy syntax and legibility enable quick testing of complex algorithms. Developers can achieve their project goals using this language and cut down on redundant processes, allowing them to focus on what’s relevant. Transferring projects makes coordination easy between developers via Python. Moreover, its active community of developers can offer invaluable assistance when developers undertake complex projects and seek guidance for the same. For all of these reasons, machine learning engineers consider Python for their artificial intelligence (AI) projects.


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Why should you use Mercer| Mettl’s Machine Learning Assessment?
Mettl's Machine Learning with Python test is specially designed to cater to the level of skills that a beginner to intermediate level Machine Learning Engineer would be expected to possess. You must use this test if you want to accurately assess the work readiness skills of candidates and find the best professionals for your organization. The demand for tech professionals, mainly ML engineers, continues to grow due to a high number of vacancies in this field, but finding high-quality candidates isn’t as easy as it seems. However, employers striving to address the demand-side challenges to hire machine learning engineers will find these assessments valuable. Mettl’s machine learning assessments are designed to reveal the strengths and weaknesses of candidates, which significantly reduces employers' time to hire.

This assessment is useful for hiring:

  • Machine Learning Engineers
  • Machine Learning Specialists
  • Machine Learning Consultants
Number of Sections 3
Number of Questions 20
Test Duration 60 Minutes
Test Language English

Section-wise details:

Number of MCA/MCQ questions based on Machine Learning Core Concepts 9
Number of MCA/MCQ questions based on  Advanced Concepts of Machine Learning 9
Number of hands-on programming problems that intend to test data analytics skills of the candidate in Python 2

Candidates with a work experience of 3-5 years are eligible to take the test

Difficulty level: Moderate


Answer to common queries:

Q. Can candidates be benchmarked based on the internal sample set?
A. Yes, at Mettl, it can be done. Should you need any further information, please write to us, and we will be happy to assist you.

Q. Is it possible to customize the report as per the need?
A. Yes, we can do it. We have already done it in the past for our clients. Should you need any further information, please write to us, and we will be happy to find the best solution for you.

Q. What are the most common machine learning interview questions?
A. Sometimes it seems quite challenging to come to terms with the stressful interview environment. Very few participants would fancy the idea of getting bombarded by complex questions during an interview. But interviewees have to prove their mettle and succeed in the recruitment process to get their dream jobs. As machine learning engineers, you need to make an excellent first impression during the interview. So, given below are some sample questions that are frequently asked at an interview:
 
Machine learning interview questions are generally based on real-world challenges and look at the problem-solving abilities of candidates. Question types may vary depending upon the nature of the business. Let’s elucidate the statement with some examples:
A digital image processing company may seek a relevant answer for this question - “What steps will you follow to figure out all the images that constitute part of a landscape?’’ A speech processing company could ask in an interview, “Amidst the pile of voicemails, how can you find the file containing the voice of an old woman?’’ A video processing company may be more interested in this question: “In a soccer video, can you mark all the times when a specific footballer is in the view?” A leading NLP company may ask a candidate, “How would you devise suggestions for the next word in an unfinished sentence?’’  There could be multiple use cases and a wide array of questions that may be asked. It is advisable to go through some case studies that relate to the company’s nature of business.
Also, given below are some questions that candidates should be prepared to answer:

  • What is the difference between labeled and unlabeled data?
  • How to use labeled data, and what if you don’t have any labeled data?
  • How do you explain skewed data?
  • How can you detect overfitting and underfitting?
  • How do you stop overfitting?
  • How do you make predictions faster?

Moreover, it’s common for interviewers to ask candidates about their previous machine learning projects that they have accomplished in a detailed manner. Interviewees need to concisely explain what challenges they faced in the past and how did they address those problems.


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