MACHINE LEARNING INTERVIEW QUESTIONS AND ANSWERS – A TECHNICAL GUIDE

With the introduction of Artificial Intelligence, Machine Learning, and Deep Learning, the world has changed and will continue to evolve in the years ahead. We’ve compiled the most commonly asked questions by interviewers in this Machine Learning Interview Questions 2021 blog. After consulting with Machine Learning Certification Training Experts, these questions were compiled.

Let’s have a look at the basic and practical kinds of question sections like machine learning interview questions for freshers and machine learning interview questions for experienced

MACHINE LEARNING INTERVIEW QUESTIONS FOR FRESHERS

  1. Tell me about Machine Learning.

Machine learning is a branch of computer science that deals with system programming to learn and develop automatically over time. For instance, robots are programmed to perform a task based on data collected from sensors. It learns programs from data on its own.

2. What do you mean by Inductive machine learning?

Inductive machine learning is the method of learning by example, in which a system attempts to infer a general rule from a collection of observed instances.

3. Tell the difference between Data Mining and Machine Learning?

The research, design, and creation of algorithms that enable computers to learn without being specifically programmed is referred to as machine learning. Data mining, on the other hand, is the method of extracting information or unknown interesting patterns from unstructured data. Machine learning algorithms are used in this process.

4. Explain Overfitting in Machine Learning?

Overfitting occurs in machine learning when a mathematical model defines random error or noise rather than the underlying relationship. Overfitting is common when a model is too complex, as a result of providing too many parameters concerning the number of training data types. The model has been overfitted, resulting in poor results.

5. Do You know the reason why Overfitting happens?

Overfitting happens and is a risk because the parameters used to train the model are not the same as the criteria used to assess the model’s efficacy.

6. List the 5 Popular Algorithms of Machine learning.

  • Decision Trees
  • Neural Networks (backpropagation)
  • Probabilistic networks
  • Support vector machines
  • Nearest Neighbor

7. What are the various Machine Learning Algorithm Techniques?

Machine Learning techniques come in a variety of forms. They are

  • Supervised Learning
  • Unsupervised Learning
  • Semi-supervised Learning
  • Reinforcement Learning
  • Transduction

8. What standard approach does supervised learning use?

The traditional method for supervised learning is to divide the example set into two parts: the training set and the test set.

9. Explain the Training set and the Test set?

A collection of data is used in various fields of information technology, such as machine learning, to discover the potentially predictive relationship known as the ‘Training Set.’ A training set is a set of examples given to the learner, while a Test set is a set of examples withheld from the learner to test the accuracy of the hypotheses developed by the learner. Training and Test sets are not the same thing.

10. In Machine learning, what are the three phases of developing hypotheses or models?

  • Model building
  • Model testing
  • Applying the model

11. Explain Algorithm Independent Machine learning?

Algorithm-independent machine learning refers to machine learning in which the mathematical foundations are independent of any single classifier or learning algorithm.

12. Tell me the difference between Artificial Intelligence(AI) and Machine Learning(ML)?

Machine Learning is the process of designing and creating algorithms based on empirical data and behavior. Artificial intelligence encompasses a variety of topics in addition to machine learning, such as information representation, natural language processing, planning, robotics, etc.

13. Tell me about Classifiers in Machine Learning?

In Machine Learning, a classifier is a method that takes a vector of discrete or continuous feature values as input and outputs a single discrete value, the class.

14. What do you know about Genetic Programming?

Genetic Programming (GP) is a subset of machine learning that uses Evolutionary Algorithms (EA). EAs are used to find immediate solutions to problems that humans are unable to solve.

15. Tell us the real-time applications of Pattern Recognition?

  • Computer Vision
  • Speech Recognition
  • Data Mining
  • Statistics
  • Informal Retrieval
  • Bio-Informatics

16. Explain Model Selection in Machine Learning?

Model selection is the method of choosing a model from among many mathematical models that are used to represent the same data set. In the fields of statistics, machine learning, and data mining, model selection is used.

17. What is Machine Learning’s Inductive Logic Programming?

ILP (Inductive Logic Programming) is a machine learning subfield that employs logical programming to reflect context information and examples.

18. What are Bayesian Networks?

The Bayesian Network is a graphical model for the probability relationship between a series of variables.

19. What are the two components of the Bayesian Logic Program?

There are two sections to the Bayesian logic program. The first is a logical part, which consists of a collection of Bayesian Clauses that capture the domain’s qualitative structure. The second part is a quantitative one that encodes the domain’s quantitative data.

20. Which technique is most commonly used to avoid overfitting?

To avoid an overfitting problem, ‘Isotonic Regression’ is used when there is enough data.

21. Define Perceptron.

Perceptron is a machine learning algorithm for the supervised classification of input into one of several non-binary outputs.

22. Explain Ensemble Learning and why is it used?

Multiple models, such as classifiers or experts, are strategically created and combined to solve a specific computational program. Ensemble learning is the name for this process. Ensemble learning is a technique for improving a model’s classification, prediction, and function approximation, among other things.

23. In the ensemble process, what is the bias-variance decomposition of classification error?

A learning algorithm’s predicted error can be broken down into bias and variance. A bias term indicates how closely the learning algorithm’s average classifier fits the target function. The variance term expresses how much the learning algorithm’s prediction varies across training sets.

24. Why is it that an instance-based learning algorithm is also known as a lazy learning algorithm?

Instance-based learning algorithms are also known as Lazy learning algorithms because they postpone induction or generalization until classification is completed.

25. Explain Dimension Reduction in Machine learning.

Dimension reduction, which can be split into feature selection and feature extraction in Machine Learning and statistics, is the method of reducing the number of random variables under consideration.

MACHINE LEARNING INTERVIEW QUESTIONS FOR EXPERIENCED

This is an attempt to assist you in passing machine learning interviews at major product companies and start-ups. Machine learning interviews at major corporations typically necessitate a detailed understanding of data models and algorithms. Such algorithm-based questions are here for your reference.

26. Tell us the difference between supervised and unsupervised machine learning?

Training labeled data is needed for supervised learning. To do classification (a supervised learning task), for example, you must first mark the data that will be used to train the model to classify data into your labeled classes. Unsupervised learning, on the other hand, does not necessitate clear data marking.

27. Explain the working of the ROC curve?

At different thresholds, the ROC curve is a graphical representation of the contrast between true positive rates and false-positive rates. It’s sometimes used as a proxy for the trade-off between the model’s sensitivity (true positives) and the fall-out, or the likelihood of a false alarm (false positives).

28. How is KNN different from k-means clustering?

K-means clustering is an unsupervised clustering algorithm, while K-Nearest Neighbors is a supervised classification algorithm. Although the mechanisms can appear to be identical at first glance, the truth is that for K-Nearest Neighbors to function, you must have labeled data to classify an unlabeled point into (thus the nearest neighbor part). Only a set of unlabeled points and a threshold are required for K-means clustering: the algorithm will take unlabeled points and eventually learn how to cluster them into categories by computing the mean of the distance between various points.

29. Do you know the reason why the Naive Bayes algorithm is said to be naive?

Despite its practical applications, especially in text mining, Naive Bayes is labeled “Naive” because it relies on an assumption that is nearly impossible to verify in real-world data: the conditional probability is calculated as the pure product of the individual probabilities of components. This necessitates complete feature independence, which is a requirement that is unlikely to be fulfilled in real life.

30. What is the difference between L1 regularisation and L2 regularisation?

L2 regularisation spreads error over all items, while L1 regularisation is more binary/sparse, with several variables assigned a 1 or 0 in weighting. Setting a Laplacean prior on the terms corresponds to L1, thus setting a Gaussian prior corresponds to L2.

31. Tell the difference between Type I and Type II error.

A false positive is a Type I error, while a false negative is a Type II error. Type I error is defined as believing something has occurred when it hasn’t, while Type II error is defined as claiming nothing is occurring when something is.

32. What is your favorite algorithm and explain it?

(Be careful when you attempt this question. Because they are testing your thoroughness at this point. Explain the algorithm which you’re very confident to present like a speech. You should be ready to answer correctly, no matter how twisted the question is)

33. Explain Decision tree pruning?

Pruning is the process of removing branches from a decision tree model that have low predictive power to reduce the model’s complexity and improve its predictive accuracy. Pruning can be done from the bottom up or from the top down, using techniques including reduced error pruning and cost complexity pruning. The simplest version of reduced error pruning is to replace each node. Keep it pruned if it doesn’t reduce predictive accuracy. Despite its simplicity, this heuristic is quite similar to a method that would optimize for full accuracy.

34. How do you make sure you are not overfitting a model?

This is a straightforward restatement of a fundamental problem in machine learning: the risk of overfitting training data and bringing the noise into the test set, resulting in incorrect generalizations.

You can avoid overfitting by following these methods.

  • Reduce uncertainty by incorporating fewer variables and parameters into the model, eliminating some of the noise from the training results.
  • Use techniques like k-folds cross-validation for cross-validation.
  • Using regularisation techniques like LASSO to penalize those model parameters that are prone to overfitting.

35. When Ensemble Techniques will be useful?

To improve predictive efficiency, ensemble techniques combine many learning algorithms. They usually help models become more stable by reducing overfitting (unlikely to be influenced by small changes in the training data).

36. Explain the Kernel trick and its uses?

Kernel functions enable in higher-dimension spaces without directly computing the coordinates of points within that dimension: instead, kernel functions calculate the inner products between the images of all pairs of data in a feature space, which is known as the Kernel trick. This gives them the very useful property of being able to calculate the coordinates of higher dimensions while being computationally cheaper than doing so explicitly. Inner products can be used to express a variety of algorithms. We can run algorithms in a high-dimensional space with lower-dimensional data by employing the kernel trick.

37. Tell us how you use the F1 score?

The F1 score is a metric for how well a model performs. It’s a weighted average of a model’s precision and recall, with scores ranging from 1 to 0, with 1 being the best and 0 being the worst. It would be used in classification tests where true negatives aren’t as relevant.

38. List the components for relational evaluation techniques.

  • Data Acquisition
  • Ground Truth Acquisition
  • Cross-Validation Technique
  • Scoring Metric
  • Significance Test
  • Query Type

39. Tell me what you know about Batch Statistical Learning?

Statistical learning techniques allow you to learn a feature or indicator from a collection of observed data that can be used to forecast data that hasn’t been seen before. Based on a statistical assumption about the data generation process, these techniques provide guarantees on the performance of the learned predictor on future unseen data.

40. List out the Supervised learning functions?

  • Classifications
  • Regression
  • Forecast time series
  • Annotate strings
  • Speech recognition

41. What are the functions of Unsupervised learning?

  • Look for data clusters.
  • Discover low-dimensional data representations.
  • Look for interesting directions in data.
  • Correlations and coordinates of interest
  • Find observations/clean up the database

42. List the two methods used in the Calibration of Supervised learning?

In Supervised Learning, there are two methods for estimating successful probabilities.

  • Platt Calibration
  • Isotonic Regression

These approaches are intended for binary classification, which is an important task.

43. Why are PCA, KPCA, and ICA used?

PCA (Principal Components Analysis), KPCA (Kernel-based Principal Component Analysis), and ICA (Independent Component Analysis) are common dimensionality reduction techniques.

44. Explain Reinforcement learning?

Reinforcement learning is a type of learning in which an agent communicates with its surroundings by performing actions and discovering errors or rewards.

It’s like being stranded on a deserted island, where you must discover the environment on your own and learn to survive and adapt to the extreme conditions. The hit-and-trial approach is used by the model to understand. It learns by receiving a reward or a penalty for each action it takes.

45. Which is better? Too many False Positives or Too many False Negatives? Explain.

It depends on the query as well as the situation for which the problem is being solved. If you’re using Machine Learning in the field of medical research, a false negative is a big risk, since the report won’t reveal any health issues even if the individual is sick.

Similarly, if Machine Learning is used to detect spam, a false positive is extremely dangerous because the algorithm can mistakenly identify a critical email as spam.

46. Model Accuracy or Model Performance. What do you think is more important?

You should be aware, however, that model accuracy is just one aspect of model efficiency. The model’s accuracy and efficiency are directly proportional, so the higher the model’s performance, the more accurate the predictions are.

47. Explain A/B testing?

Statistical hypothesis testing for a randomized experiment with two variables A and B are known as A/B. It’s used to compare two models that use different predictor variables to see which one matches a given set of data the best.

Consider the following scenario: you’ve built two models (each with its own set of predictor variables) that can be used to suggest goods on an e-commerce platform.

These two models can be compared using A/B Testing to see which one better recommends services to a consumer.

48. Tell us in detail about Cluster Sampling?

It’s a method of selecting unified groups with similar characteristics at random from a given population.

A cluster sample is a probability sample in which each sampling unit is a group of elements.

Managers (samples) will represent elements, and companies will represent clusters if you’re clustering the total number of managers in a group of companies.

49. How can imbalanced datasets be handled?

If you have a classification test, for example, and 90% of the data is in one class, you have an imbalanced dataset. This causes issues: a 90% accuracy can be skewed if you don’t have any predictive capacity on the other type of data! Here are several strategies for getting over the imbalance:

  • Collect more data to even out the dataset’s imbalances.
  • To compensate for imbalances, resample the dataset.
  • On your dataset, try a different algorithm entirely.

50. What are the various categories in which the sequence learning process can be classified?

  • Sequence prediction
  • Sequence generation
  • Sequential decision
  • Sequence recognition

CONCLUSION:

We hope that this blog article’s list of machine learning interview questions will assist you in preparing for your next machine learning interviews. Also, Check out our NSCHOOL Academy’s Machine learning course and certification process that imbibe your aspired technical knowledge in your career.

Now, prepare the above questions to enrich your knowledge and wish you all the best for your Machine learning interview!