Updated June 15, 2023
Introduction to Machine Learning Interview Questions
The following article provides an outline for Machine Learning Interview Questions. Machine Learning is an approach to artificial intelligence. This allows every system to automatically learn and improve without being programmed explicitly. Machine Learning helps develop computer programs that can access and use data to learn for themselves. When the statistical model raises a random error or is excessively complex, Machine Learning helps solve those complexities.
You have finally found your dream job in Machine Learning but are wondering how to crack the Machine Learning interview and what could be the probable 2023 Machine Learning interview questions. Every interview is different, and the job scope is different too. Keeping this in mind, we have designed the most common Machine Learning Interview Questions and Answers to help you get success in your interview.
Part 1 – Machine Learning Interview Questions (Basic)
This first part covers the basic Interview Questions and Answers:
Q1. What do you understand by Machine Learning?
Answer:
Machine learning is an application of artificial intelligence that allows systems to learn and improve from experience without being explicitly programmed automatically. Machine learning focuses on developing computer programs that can access and use data to learn for themselves.
Q2. Give an example that explains Machine Leaning in the industry.
Answer:
Robots are replacing humans in many areas. It is because robots are programmed such that they can perform tasks based on data they gather from sensors. They learn from the data and behave intelligently.
Q3. What are the different Algorithms and techniques in Machine Learning?
Answer:
The different types of Algorithm techniques in Machine Learning are as follows:
- Reinforcement Learning
- Supervised Learning
- Unsupervised Learning
- Semi-supervised Learning
- Transduction
- Learning to Learn
Q4. What is the difference between supervised and unsupervised machine learning?
Answer:
These are the basic Machine Learning Interview Questions asked in an interview. Supervised learning is a process where it requires training labeled data, while Unsupervised learning doesn’t require data labeling.
Q5. What is the function of Unsupervised Learning?
Answer:
The function of Unsupervised Learning is as below:
- Find clusters of the data.
- Find low-dimensional representations of the data.
- Find interesting directions in data.
- Interesting coordinates and correlations.
- Find novel observations.
Q6. What is the function of Supervised Learning?
Answer:
The function of Supervised Learning is as below:
- Classifications
- Speech recognition
- Regression
- Predict the time series.
- Annotate strings
Q7. What are the advantages of Naive Bayes?
Answer:
- The classifier will converge quicker than discriminative models.
- It cannot learn the interactions between features.
Q8. What are the disadvantages of Naive Bayes?
Answer:
- It is because the problem arises for continuous features.
- It makes a solid assumption about the shape of your data distribution.
- It can also happen because of data scarcity.
Q9. Why is naive Bayes so naive?
Answer:
Naive Bayes is so innocent because it assumes that all of the features in a dataset are equally important and independent.
Q10. What is Overfitting in Machine Learning?
Answer:
These are the popular Machine Learning Interview Questions asked in an interview. Overfitting in Machine Learning is when a statistical model describes random error or noise instead of the underlying relationship or when a model is excessively complex.
Q11. What are the conditions when Overfitting happens?
Answer:
One of the fundamental reasons and the possibility of Overfitting is that the criteria used for training the model are different from the criteria used to judge the efficacy of a model.
Q12. How can you avoid Overfitting?
Answer:
We can avoid overfitting by using the following:
- Lots of data
- Cross-validation
Part 2 – Machine Learning Interview Questions (Advanced)
Let us now have a look at the advanced Interview Questions.
Q13. What are the five popular algorithms for Machine Learning?
Answer:
Below is the list of five popular algorithms of Machine Learning:
- Decision trees
- Probabilistic networks
- Nearest neighbor
- Support vector machines
- Neural networks
Q14. What are the different use cases where machine learning algorithms can be used?
Answer:
The different use cases where machine learning algorithms can be used are as follows:
- Fraud detection
- Face detection
- Natural language processing
- Market segmentation
- Text categorization
- Bioinformatics
Q15. What are parametric models and nonparametric models?
Answer:
Parametric models are those with a finite number of parameters, and to predict new data, you only need to know the model’s parameters. Non Parametric models are those with an unbounded number of parameters, allowing for more flexibility, and to predict new data, you need to know the parameters of the model and the state of the data that has been observed.
Q16. What are the three stages of building the hypotheses or models in machine learning?
Answer:
These are the frequently asked Machine Learning Interview Questions in an interview.
The three stages of building the hypotheses or model in machine learning are:
- Model building
- Model testing
- Applying the model
Q17. What is Inductive Logic Programming in Machine Learning (ILP)?
Answer:
Inductive Logic Programming (ILP) is a subfield of machine learning which uses logic programming to represent background knowledge and examples.
Q18. What is the difference between classification and regression?
Answer:
- Classification is about identifying group membership, while the regression technique involves predicting a response.
- Classification and Regression techniques are related to the prediction.
- Classification predicts belonging to a class, whereas regression predicts the value from a continuous set.
- Classification technique is preferred over regression when the model results need to return the belongingness of data points in a dataset with specific explicit categories.
Q19. What is the difference between inductive machine learning and deductive machine learning?
Answer:
Machine learning is where the model learns by examples from a set of observed instances to draw a generalized conclusion. In contrast, in deductive learning, the model concludes, and then the conclusion is drawn.
Q20. What are the advantages of decision trees?
Answer:
The advantages of decision trees are:
- Decision trees are easy to interpret.
- Nonparametric.
- There are relatively few parameters to tune.
Q21. What are the disadvantages of decision trees?
Answer:
Decision trees are prone to overfit. However, this can be addressed by ensemble methods like random forests or boosted trees.
Q22. What are the advantages of neural networks?
Answer:
These are the advanced Machine Learning Interview Questions asked in an interview. Neural networks have led to performance breakthroughs for unstructured datasets such as images, audio, and video. Their incredible flexibility allows them to learn patterns that no other Machine Learning algorithm can learn.
Q23. What are the disadvantages of neural networks?
Answer:
Neural Network requires a large amount of training data to converge. It’s also challenging to pick the exemplary architecture, and the internal “hidden” layers are incomprehensible.
Q24. What is the difference between L1 and L2 regularization?
Answer:
- L1/Laplace tends to tolerate both large values as well as minimal values of coefficients more than L2/Gaussian.
- L1 can yield sparse models, while L2 doesn’t.
- L1 and L2 regularization prevents Overfitting by shrinking the coefficients.
- L2 (Ridge) shrinks all the coefficients by the same proportions but eliminates none, while L1 (Lasso) can shrink some coefficients to zero, performing the variable selection.
- L1 is the first-moment norm |x1-x2| that is simply the absolute distance between two points, whereas L2 is the second-moment norm corresponding to Euclidean Distance that is |x1-x2|^2.
- L2 regularization tends to spread error among all the terms, while L1 is more binary/sparse.
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