10 Machine Learning Interview Questions - Frequently Asked

Machine learning has grown in popularity so fast over the last decade. Machine learning is a branch of artificial intelligence that has made it possible to accurately analyze and gain valuable insights into risks and opportunities from the vast volumes of data generated every day. As of 2018, the global machine learning market was worth $ 6.9 billion and was projected to grow at a CAGR of 43.8% through 2025. This remarkable growth is a clear indication of how much AI and ML have caught the world’s interest.

All of a sudden, AI and ML skills are in skyrocketing demand as organizations seek to make the most of the huge volumes of data available to them.

Are you interested in pursuing a career in AI?

Have you undertaken an AI and ML course and can demonstrate your skills in real-world situations?

10 frequently asked machine learning interview questions

Are you preparing for an interview or a position in ML?

The time has never been riper than now to take advantage of the skills shortage and massive opportunities for AI and ML professionals in the job market.

However, you need to be polished because the world needs refined talent and an in-depth understanding of ML concepts to spur innovation and advancement in this evolving field. Whilst there are so many ML professionals out there, recruiters are looking out for real talent. So, how do you ensure that you ace that interview and impress the panel?

Here are some common machine learning interview questions that you are bound to be asked in an interview.

1. 1. How can your machine learning skills and competencies help grow business revenue?  

The way you will answer this question demonstrates your knowledge of machine learning, core driving factors, as well as its impact on different aspects of the business. It also demonstrates the specific machine learning skills that you possess.

Depending on the organization and position you are being interviewed for, you could, for instance, answer that your skills in developing recommender systems will leverage customer data, the right tools, and technologies to understand customer behavior and expectations to develop more personalized recommendations for customers. This has a positive impact on revenue by lowering marketing costs and increasing conversion rates and overall customer experience.

2. 2. What is the difference between inductive machine learning and deductive machine learning?  

Inductive learning is ML models that are programmed to learn using observations to draw general inferences.

Deductive learning, on the other hand, applies conclusions that have already been drawn from observations to draw a valid inference.

3. 3. With a dataset available, how do you determine the most appropriate ML algorithm to apply to it for analysis?  

The type of machine learning algorithm to be used is determined majorly by the type of data in a dataset. While there is no laid down procedure for choosing the right algorithm, performing exploratory data analysis to understand the nature and purpose of the dataset.

Ideally, linear regression is used for linear data and neural networks for unstructured datasets with images, audio, videos, and other data types. The table below offers guidelines for performing data exploration.

Type of machine learning	Purpose	Most likely algorithm
Supervised machine learning	Classification Regression Estimation	Neural networks Support vector machines Bayesian networks
Unsupervised machine learning	Clustering Prediction	K-means Gaussian Mixture Model Mixture Model
Reinforcement learning	Decision making	Q-learning R-learning TD learning

4. 4. What is the difference between KNN and k-means clustering?

Both KNN (K-Nearest Neighbour) and k-means clustering are machine learning algorithms.

KNN is a supervised learning algorithm used for classification. This algorithm works with labeled data by classifying data into an unlabeled data point, in this case K. Prediction is done by voting the most frequent labels closest to the unlabeled point.

K-means clustering is an unsupervised learning algorithm used for clustering. Thus, it uses different unlabeled data points around which data is clustered, determined by the mean distance between different data points.  

5. 5. Distinguish between an array and a linked list

An array refers to a data structure with a fixed number of a single type of data element. Arrays are often implemented as the default data structure. An array is predefined. As such, it has to be re-defined for it to grow organically.

A linked list is a collection of data elements of the same type whose order is in such a way that each element connects to the next in a sequence using pointers. For this reason, the size of a linked list is variable and can grow organically by adding more elements of the same type in the sequence.

6. 6. Why random forests and why are they preferred to individual decision trees?

Random forests are a type of ensemble model. Random forest models thrive on the principle of the “wisdom of the crowd” in which the random forests aggregates outcomes from multiple trees. This model is considered more accurate than decision trees as it generalizes its search to select the best feature among a random subset of features in multiple trees. Also, random forests prevent overfitting.

On the other hand, while decision trees are easy to compute, they are prone to overfitting hence lower accuracy.

7. 7. Outline the steps to take when pruning a decision tree

Pruning of a decision tree is done to the nodes with weak predictive powers or those that cause overfitting to lower the complexity of the model and optimize its predictive accuracy. There are two approaches to pruning a decision tree. These are

• Bottom-up in which pruning is done starting with the last node
• Top-down in which pruning is done staring at the root node

8. 8. Which approaches can be employed to evaluate the performance of a machine learning model?

• First, start by splitting your data set into two, the training set and the test set. You can apply the cross-validation technique to further subdivide the datasets into smaller sets. The test set will be used to test the accuracy of the model developed by the training set while a data validation set is used to evaluate the model while still building it.
• Define the metrics to use to evaluate the performance of an ML model. These may include classification metrics used to measure the F1 accuracy score in a confusion matrix, and regression metrics that compare the variance between the actual and expected outcome.

9. 9. What is the tradeoff between model accuracy and model performance?  

Model accuracy is a subset of model performance. Thus focusing on model accuracy alone can be misleading. Because model accuracy is directly proportional to model performance, the higher the performance of a model the more accurate it will be in its predictions.

10. 10. What is a recommender system?

A recommender system is a subclass of information filtering systems whose purpose is to suggest (recommend) relevant items to users during their search. Based on a user’s historical data, recommender systems are programmed to predict a user’s preference for certain products by providing similar suggestions to make it easy for the user to find what he/she is looking for. Recommender systems have widely been adopted in movies, retail products, music streaming, and news sites.

Conclusion

Machine learning has grown to become an essential part of our daily lives. Machine learning models are developed with an ability to learn and improve from input data to make accurate inferences and predictions to facilitate decision-making. For this reason, the importance of developing high-performing and accurate models for specific purposes cannot be overemphasized. The full potential of ML is yet to be realized and the future for ML professionals is promising. Grasping concepts is not enough. A seasoned professional should possess the skills and competence to apply their knowledge of ML to real-world situations while also advancing innovation and maturity of this emerging field through research and development.