Machine learning interview preparation tips
Machine learning (ML) has become central to every big company’s operations, whether Facebook, Google, or Microsoft. Its ability to automate tasks and solve complex problems has made it one of the most popular technologies in multiple fields, such as marketing, finance, software, transportation, and healthcare.
So machine learning specialists are highly in demand. Recent statistics state the global ML market is projected to reach USD 79.29 billion by the end of 2024 and showcase an annual growth rate of 36.08%, resulting in a market volume of USD 503.4 billion by 2030.
Competition is high, and to stand out from millions, you’ll have to ace the machine learning interviews that come your way.
What are employers looking for?
Employers are looking for well-rounded professionals who are polished when it comes to their knowledge, adaptability, and ambition in the field of machine learning. Before the interview prep, look at the skills you should have on a broader spectrum.
Technical skills
A strong foundation in mathematics is necessary for machine learning. Linear algebra, statistics, probability, and calculus are just some concepts you must be proficient in—these are the backbone of most ML algorithms.
Fluency in programming languages such as Python and R is a requirement, and a grasp of ML libraries and frameworks such as TensorFlow, PyTorch, scikit-learn, and pandas is valuable, especially for statistical analysis, data manipulation, numeral computing, and more.
Technical skills also involve knowledge in programming, data processing, model evaluation, etc. Familiarity with the end-to-end model deployment processes is also necessary, as is experience with big data technologies such as Hadoop and Spark and cloud platforms such as AWS, Google Cloud, and Azure.
Practical experience
Employers prefer candidates who don’t just hoard knowledge but know how to effectively apply what they learn. Gaining practical experience shows your expertise; end-to-end ML projects, domain-specific projects, big data, and distributed computing are some areas where you can prove your expertise.
Collaborative projects within a team and participating in competitions and hackathons are other interesting ways you can showcase your passion and knack for problem-solving to your future employer.
Knowing the business
Whatever industry you are applying in, it is necessary to understand it deeper beyond the surface. Employers look for machine learning professionals who prioritize working toward business goals, and it is only possible to design effective machine learning solutions if you are familiar with the domain you are working in.
Ethical awareness
Machine learning and AI are connected to multiple ethical implications, something employers are increasingly becoming aware of, such as:
Bias and transparency: ML models can perpetuate or amplify biases present in training data, leading to various consequences, such as unfair treatment of individuals in areas like hiring and law enforcement. For this reason, AI decisions should be transparent, especially in high-stakes applications. Understanding responsible AI helps prevent such biases.
Legal and regulatory compliance: AI systems are increasingly under scrutiny by regulators, and aspiring ML professionals must ensure that the models they create comply with user privacy and other associated laws.
Long-term viability: Negative outcomes such as discrimination and harm are always possible with irresponsible AI. ML professionals must design robust systems that align with societal values. Responsible AI practices ensure that ML and AI technologies are sustainable for the long term, avoiding unethical practices.
Innovation: Responsible AI has a competitive advantage—companies whose ML professionals push forward with strong values can differentiate themselves from competitors, driving innovation for reliable and more resistant systems.
Risk management: AI is not invincible, and in cases where AI systems may fail or cause harm, a strong foundation that can help minimize damage is necessary.
Machine learning interview format
Companies may have their own formats for interviewing candidates for a machine learning interview. This brief overview can help you know what to expect once you have been shortlisted.
Initial screening
The initial screening round is usually non-technical and conducted by a recruiter or a hiring manager. The main objective is to check whether the candidate qualifies for the minimum requirements for the role. You will be asked about yourself, your work history, and other relevant qualifications.
You may be asked basic technical questions about ML concepts such as algorithm types, evaluation metrics, or statistics. For this stage, you must be familiar with the information you’ve put down in your resume and be honest about your skills.
Technical screening
The technical screening, also known as the algorithm design or coding round, aims to filter out candidates who can write and optimize code.
You may face a coding challenge where you must solve programming problems within a time limit on a shared coding platform such as HackerRank, CodeChef, or LeetCode. In this case, the interviewer will not be involved.
In another case, you could also end up with a coding interview, where you are asked questions related to machine learning topics such as decision trees, neural networks, regularization, etc. The questions may also revolve around math and statistics to understand your base understanding; your problem-solving approach, fluency, and skills will be tested.
Machine learning round
If you make it to this round, be prepared for a deep dive. Here, you will be tested on both your primary and in-depth knowledge of machine learning.
The focus could be on designing a model, explaining how you would handle a data-related challenge, or you may be given a dataset and asked to analyze it and explain your approach. You must be familiar with machine learning libraries such as Keras, NumPy, and Scikit-learn to help you with the tests in this round.
Some commonly asked questions will revolve around machine learning concepts such as linear regression, logistic regression, SVM, KNN, ensemble learning methods, artificial neural networks (ANN), clustering, recurrent neural networks (RNN), feature engineering, data processing and visualization, loss functions, error metrics, and more.
Remember System Design and its associated concepts, such as ingestion, model training, deployment, and monitoring. You may also be given tasks related to coding and algorithms and tested on your ability to implement machine learning algorithms from scratch.
Project presentation
Depending on the industry you are applying in, you may face a technical presentation round where you present a past project to a panel of experts or be given a machine learning case study problem rooted in a real-world context to solve. Besides your technical insight and problem-solving skills, communication is a key element you will be tested on.
Final round
Pat yourself on the back for reaching the final round, but don’t get too comfortable just yet. Besides the interviewer evaluating your career goals and whether you’ll be a cultural fit, you may be faced with a technical discussion around your approach to machine learning, knowledge of industry trends, and what your strategic approach to projects looks like.
Common machine learning interview questions
What are the different types of machine learning (ML)?
There are three main types of machine learning: supervised learning, unsupervised learning, and reinforcement learning.
Supervised: It learns a function that maps an input to an output based on sample input-output pairs. It uses labeled training data and new data.
Unsupervised: It analyzes unlabeled datasets without human intervention. Its primary use is extracting generative features, identifying structures and trends, grouping results, etc. The model is trained on a dataset that contains only input data without corresponding output labels.
Reinforcement: This enables software agents and machines to evaluate the optimal behavior in a specific context or environment to enhance efficiency. It involves learning from feedback (rewards or penalties) received after conducting specific actions.
Differentiate between training sets and test sets.
The three-step process of creating a model involves training, testing, and deploying it. While the training set provides examples for the machine learning model to analyze, the test set is used to judge the accuracy of the hypothesis generated by the model and provides an unbiased result.
What’s the difference between deep learning, AI, and machine learning?
Artificial intelligence (AI) is the concept of simulated human intelligence in machines that can be programmed to think, learn, and solve problems. Machine learning is a subset of AI that creates algorithms and statistical models to learn patterns from data and make predictions or decisions based on them. Lastly, deep learning is a subset of machine learning that uses neural networks with multiple layers to model complex patterns in large amounts of data.
What is overfitting, and how do you avoid it?
Overfitting occurs when a machine learning model learns the training set to the extent that it starts taking in noise and random fluctuations in training data sets as concepts. This causes a failure to generalize new, unseen data, resulting in poor performance on the test set, particularly in real-world applications.
The various ways to avoid overfitting include regularization, making a simple model with fewer variables and parameters, data augmentation, dropout (neural networks), cross-validation methods like k-folds, pruning decision trees, and regularization techniques such as LASSO.
When will you use classification over regression?
Classification is used when the goal is to predict a categorical outcome, while regression is used when your target variable is continuous. Both are supervised machine learning algorithms.
Final words
If you are confident in your machine learning knowledge and skills, the only course of action is to stay calm and walk into that interview with your head high.
Do a last review of the machine learning fundamentals with Educative’s Machine Learning Theory and Practice module.
