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Advanced Machine Learning Interview Questions You Should Practice

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Given that machine learning is the world's future, it would be an understatement to say that it is an important field. As a result, there is a high demand for skilled machine learning professionals who can contribute to the field's advancement.

The interview process for these positions is quite rigorous, so you should prepare accordingly. To get you started, we've compiled a list of the most frequently asked advanced machine learning interview questions.

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To give you a better understanding of what kind of advanced machine learning interview questions you can expect, in this article, we’ll cover:

  • Advanced Machine Learning Interview Questions and Answers
  • Sample Advanced Machine Learning Interview Questions for Practice
  • FAQs on Advanced Machine Learning Interview Questions

Advanced Machine Learning Interview Questions and Answers

Some popular and advanced machine learning interview questions, along with answers, have been given below:

Q1. Define P-value.

When a decision has to be made about a hypothesis test, P-values are used. The P-value is the minimum level at which a null hypothesis is rejected. The lower the p-value, the likelier you’ll reject the null hypothesis.

Q2. What do you mean by Reinforcement Learning?

Unlike the other kinds of learning, such as supervised and unsupervised, neither data nor labels are provided in reinforcement learning. Our learning depends on the rewards provided to the agent by the environment.

Q3. How does one check the Normality of a dataset?

When looking at it visually, certain plots can be used. Some normality checks have been given below:

  • Shapiro-Wilk Test
  • Anderson-Darling Test
  • Martinez-Iglewicz Test
  • Kolmogorov-Smirnov Test
  • D’Agostino Skewness Test

Q4. Explain a Random Forest and its functioning. 

A versatile machine learning method that can perform both — regression and classification tasks — is known as a random forest. Like bagging and boosting, this method combines a set of other tree models.

It creates a tree using a random sample from the columns in the test data. The steps involved in the creation of trees in a random forest are:

  • Procure a sample size from the training data.
  • Start with a single node.
  • Using the start node, run the algorithm given below:
  1. Stop if the number of observations in total is less than the node size.
  2. Pick random variables.
  3. Figure out the variable responsible for doing the ‘best’ job of splitting the observations.
  4. Divide the observations into two nodes.
  5. Implement step ‘a’ on both these nodes.

Q5. How would you define a neural network?

A simplified model of the human brain is known as a neural network. Like the brain, the model has neurons that activate when they encounter something similar. The different neurons are connected through the connections that provide information flow from neuron to neuron.

Learn about the various Machine Learning Engineering Roles and know what’s the best fit for you.

Q6. How to deal with overfitting and underfitting?

Overfitting refers to the model that is fitted to training data well. When it comes to this case, the data needs to be resampled, and the model accuracy needs to be estimated using techniques such as k-fold cross-validation.

On the other hand, in the case of underfitting, we can’t understand or gather the patterns from the data. We either have to change the algorithms or feed more data points to the model when this happens. 

Q7. Define ensemble learning. 

The process of combining various machine learning models to develop more powerful models is known as ensemble learning. Now, a model can be different for many reasons. Some of these are:

  • Different Population
  • Different Hypothesis
  • Different modeling techniques

As one works with the model’s training and testing data, an error occurs. It might just be a bias, variance, and irreducible error. The model always needs to strike a balance between bias and variance. This is called a bias-variance trade-off. Essentially, ensemble learning is a way that’s used to perform this trade-off.

Many ensemble technicals are available. However, when aggregating multiple models, usually there are only two methods: 

  • Bagging (native method): In this method, you take the training set and then generate new ones from it.
  • Boosting (more elegant method): This method is similar to bagging. It is used to optimize the best weighing schemes for a training set.

Q8. How to know which machine learning algorithm to use?

The answer to this question is dependent on the dataset you have. Linear progression is used whenever the dataset is continuous. There isn’t any particular way that determines which ML algorithm should be used. It varies based on the exploratory data analysis (EDA).

You can think of EDA as something that ‘interviews’ the dataset. Now, as a part of this interview, the following things are done:

  • Segregation of variables as continuous, categorical, and so on. 
  • Summarization of variables using descriptive statistics.
  • Visualization of variables using charts.

Depending on the above observations, you choose the algorithm that best fits the particular dataset. 

Q9. How should outlier values be handled?

An observation in the dataset that is pretty far from the others in the dataset is known as an outlier. The following tools can be used to discover outliers:

  • Box plot
  • Z-score
  • Scatter plot, etc.

Usually, three simple strategies can be followed to handle outliers:

  • Drop them.
  • Mark them as outliers and then include them as a feature.
  • Similarly, the feature can be transformed to decrease the effect of the outlier.

Q10. How can you select K for K-means Clustering?

To select K, two methods can be used. These are:

  • Direct methods: These contain elbow and silhouette.
  • Statistical testing methods: These have gap statistics.

Most often, the silhouette is used when the optimal value of k has to be determined.

These are some of the most important advanced machine learning interview questions. So when you prepare your study plan, make sure to include these. 

Sample Advanced Machine Learning Interview Questions for Practice

Here are some sample advanced machine learning interview questions that’ll surely take your preparation to the next level:

  1. If there is a data set with missing values spread along 1 standard deviation from the median, define the percentage of data that’ll remain unaffected.
  2. Explain why XGBoost performs better than SVM.
  3. List the various stages involved in the development of an ML model.
  4. When using scikit-learn, do we need to scale our feature values when they vary greatly?
  5. Suppose a dataset has 50 variables. But out of these, 8 have values higher than 30%. How can this be addressed?
  6. What is the difference between the normal soft margin SVM and SVM with a linear kernel?
  7. Define loss and cost functions. What is the primary difference between them?
  8. What do you mean by a generative model?
  9. Explain the primary differences between classical and Bayesian statistics.
  10. What is Bayes’ theorem, and how does it work?
  11. How would you explain the functioning of a recommendation system?
  12. What would your criteria be if you had to evaluate a regression model based on R², adjusted R², and tolerance?
  13. Define PCA and its function.
  14. How does unsupervised learning differ from supervised learning?
  15. Linear regression models are usually evaluated using Adjusted R² or an F value. How is a logistic regression model evaluated?

These are the kind of advanced machine learning interview questions you can expect. Further, this is a field that advances fast and is constantly evolving. So make sure that you’re up-to-date with the latest advancements before you arrive for that interview and are prepared accordingly. You can also look at some tips and best formats to create a Machine Learning Engineer Resume.

If you want to understand how machine learning interviews at FAANG are conducted, read Google Machine Learning Engineer Interview Process.

FAQs on Advanced Machine Learning Interview Questions 

Q1. What do you mean by machine learning?

In simple words, machine learning is a subfield of artificial intelligence defined as the ability of a machine to replicate intelligent human behavior. AI systems carry out complicated tasks like how humans solve problems.

Q2. What are the 3 main types of machine learning tasks?

Machine learning can be divided into three types — supervised learning, unsupervised learning, and reinforcement learning.

Q3. What is bias in machine learning?

The phenomenon that changes the result of an algorithm in favor of or against a particular idea is known as a bias. It is considered a systematic error in the machine learning model because of incorrect assumptions in the machine learning process.

Q4. List some advanced machine learning interview questions.

Some advanced machine learning interview questions are — Explain the Fourier Transformation in Machine Learning. Define bagging and boosting in machine learning. What is meant by cross-validation? Is it possible to manage imbalanced datasets?

Q5. What are machine learning interview questions for freshers?

For freshers, some machine learning interview questions are — Define machine learning, artificial intelligence, and deep learning. How is the k-nearest algorithm different from the KNN clustering? Explain the ROC curve and its working.

We Can Help You Prepare Machine Learning Interview Questions 

If you’re looking for guidance as you prepare for advanced machine learning interview questions, you can check out our Machine Learning Interview Course.

Interview Kickstart offers interview preparation courses taught by FAANG tech leads and seasoned hiring managers. We have trained thousands of software engineers to crack the most challenging interviews at Google, Facebook, Amazon, Apple, Netflix, and other top tech companies.

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