machine learning interview questions

Machine Learning and Artificial Intelligence has now become a hot button topic in the field of information technology! So if you wish to start your career in this path, here we’re to help you! In this tutorial guide, we have come up with the top most important and most frequently asked Machine Learning Interview Questions that can get you the clear picture on what you can expect when you attend the interview. Here we gathered important Machine Learning Interview Questions from professionals who are under the interview panel. And we assure that we have included almost 90% of the most common and frequently asked question. Anyhow, don’t forget to book mark this page, as we update the page with more and more Machine Learning Interview Question soon. Come on, let us discuss the top 20+ Machine Learning Interview Questions for Freshers and Experienced Candidates.

Frequently Asked Machine Learning Interview Questions

Question: Difference Between Machine Learning and Data Mining
Answer:

Machine learning defines the design and development of programs and algorithms that give computers the ability to learn without being explicitly programmed. But when it comes to data mining, the unstructured or unorganized data tries to extract knowledge from the database.

Question: Define Overfitting in Machine Learning
Answer:

In machine learning, instead of underlying relationship, when a statistical model states a random error or noise, overfitting occurs. Overfitting is normally observed when a model is extremely complex. The model exhibits poor performance which has been overfit.

Question: Why overfitting Happens?
Answer:

If the criteria used for the training model and the criteria used to judge the efficacy of a model are different, overfitting happens.

Question: What You Can Do To Avoid Overfitting?
Answer:

It has been stated that overfitting usually occurs if you have a small dataset. So, by using a lot of data, overfitting can be avoided. But in case, if you have small data set and you are forced to come with a model based on the small data set, then you can prevent overfitting by using the method called Cross Validation.

In this cross validation method, dataset splits into two section, testing and training datasets, the testing dataset will only test the model while, in training dataset, the datapoints will come up with the model.

Question: List Out the Top Most Popular Algorithms in Machine Learning
Answer:
  • Decision Trees
  • Neural Networks (back propagation)
  • Probabilistic networks
  • Nearest Neighbor
  • Support vector machines
Question: Define The Three Stages to Build a model in Machine Learning
Answer:
  • Model Building
  • Model Testing
  • Applying the Model
Question: Mention the Different Algorithms Used in Machine Learning
Answer:
  • Supervised Learning
  • Unsupervised Learning
  • Semi-supervised Learning
  • Reinforcement Learning
  • Transduction
  • Learning to Learn
Question: Define Training Set and Test Set
Answer:

Training Set: In Machine Learning, a set of data used to discover the predictive relationship is known as Training Set.

Test Set: Whereas Test Set is defined as it is used to test the accuracy of the algorithm prepared by the learner, and it is the set of example held back from the learner.

Question: What Are The Approaches Used in Machine Learning
Answer:
  • Concept Vs Classification Learning
  • Symbolic Vs Statistical Learning
  • Inductive Vs Analytical Learning
Question: Explain the Role of Unsupervised Learning?
Answer:
  • Find clusters of the data
  • Find low-dimensional representations of the data
  • Find interesting directions in data
  • Interesting coordinates and correlations
  • Find novel observations/ database cleaning
Question: Explain the Functions of Supervised Learning
Answer:

In the context of both Artificial Intelligence (AI) and Machine Learning, Supervised Learning is the kind of system in which both input and expected output data are provided. Input and output data are labelled for classification to provide a learning basis for future data processing.

Question: Define Algorithm Independent Machine Learning
Answer:

Machine learning in where mathematical foundations are not dependent on any particular classifier or learning algorithm is referred as algorithm independent machine learning

Question: Difference between Machine Learning and Artificial Intelligence
Answer:

Designing algorithms according to the behavioral based on the empirical data is called as Machine Learning. Whereas, in addition to Machine Learning, Artificial Intelligence also covers all other aspects like Knowledge representation, natural language processing, data planning, robotics and so on.

Question: Define the Role of Classifier in Machine Learning
Answer:

In Machine Learning, the classifier acts as the system that inputs a vector of discrete or continuous feature values and outputs a single discrete value, the class.

Question: What Are The Benefits of Naive Bayes?
Answer:

In Naïve Bayes classifier will converge quicker than discriminative models like logistic regression, so you need less training data.  The main advantage is that it can’t learn interactions between features.

Question: List Out the Areas Where Pattern Recognition is Used?
Answer:
  • Computer Vision
  • Speech Recognition
  • Data Mining
  • Statistics
  • Informal Retrieval
  • Bio-Informatics
Question: Define Generic Programming
Answer:  

Generic programming is one of the two techniques used in Machine Learning. This programming is based on the testing and selecting the best choice among a set of data results.

Question: Define Model Selection in Machine Learning
Answer:

The process of selecting models among different mathematical models, which are used to describe the same data set is known as Model Selection. Model selection is applied to the fields of statistics, machine learning and data mining.

Question: List Out the Two Methods Used for the Calibration in Supervised Learning
Answer:
  1. Platt Calibration
  2. Isotonic Regression
Question: Name the Method Which is Frequently Used to Prevent Overfitting
Answer:

‘Isotonic Regression’ is used to prevent an overfitting issue when there is sufficient data.

Question: What is the Role of Ensemble Learning
Answer:

Ensemble learning is used when you build component classifiers that are more accurate and independent from each other.

Question: What Are the Components of Evaluation Techniques?
Answer:
  • Data Acquisition
  • Ground Truth Acquisition
  • Cross Validation Technique
  • Query Type
  • Scoring Metric
  • Significance Test
Question: Define Batch Statistical Learning
Answer:

Statistical learning techniques allow learning a function or predictor from a set of observed data that can make predictions about unseen or future data. These techniques provide guarantees on the performance of the learned predictor on the future unseen data based on a statistical assumption on the data generating process.

Question: Define Deep Learning
Answer:

Deep learning is a subset of Machine learning. It mostly deals with neural networks: how to use back propagation and other certain principles from neuroscience to more accurately model large sets of unlabeled data. In a nutshell, Deep Learning represents unsupervised learning algorithm that learns data representation mainly through neural networks.

Question: Define Pruning in Decision Trees
Answer:

Pruning is you remove branches that have weak predictive power in order to reduce the complexity of the model and in addition increase the predictive accuracy of a decision tree model. There are several flavors which includes, bottom-up and top-down pruning, with approaches such as reduced error pruning and cost complexity pruning.

Question: What is Convex Hull?
Answer:

Convex hull represents the outer boundaries of the two groups of data points. Once convex hull is created, we get maximum margin hyperplane (MMH), which attempts to create the greatest separation between two groups, as a perpendicular bisector between two convex hulls.

Practical Machine Learning Interview Questions

Question: Do You Have Research Experience In Machine Learning?
Answer:

Machine Learning is booming so no one wants beginners or novice players in their teams. Most employers hiring for Machine Learning position will look for your experience in the field. Research papers, co-authored or supervised by leaders in the field, can set you apart from the herd. Make sure you are ready with all the summary and justification of the work you have done in the past years.

Question: What Are the Recent Machine Learning Papers You Have Read?
Answer:

As this field is emerging day by day, it is crucial to keep up with the latest scientific literatures to show that you are really into Machine Learning and not here just because it is the latest buzzword. Some good books to start with includes Deep Learning by Ian Goodfellow.

Question: What Is Your View About Google Self Driving Cars?
Answer:

Questions like this check your understanding of current affairs in the industry and how things at certain level works. Google is currently using recaptcha to source labelled data on storefronts and traffic signs. They are also building on training data collected by Sebastian Thrun at GoogleX.

Question: How Can We Use Your Machine Learning Skills To Generate Revenue?
Answer:

This is a tricky question, I would say. The ideal answer would demonstrate knowledge of what drives the business and how your skills could relate. To demonstrate, you can remark that your skills at developing a better recommendation model would remarkably increase user retention, which would then increase revenue in the long run.

Question: Explain Machine Learning To Me Like A 5-Year-Old.
Answer:

Make sure to explain the process of Machine learning from the basics. Also, don’t use complex terms, make the concepts simpler; and at the same time cover the entire meaning of Machine Learning.

That’s it! Hope we have covered everything from the basic Machine Learning Interview Questions to the practical Machine Learning Interview Questions. If you like this tutorial guide, do share with your friends and on all social media platforms. Also, don’t forget to share your views in the comments and let other readers give insights to how you think.

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