Introduction To Machine Learning Interview Questions and Answers
Machine Learning is an approach to artificial intelligence. This provides an ability to every system such that it automatically learn and improve without being programmed explicitly. Machine Learning helps in the development of computer programs that can access data and use it to learn for themselves. When the statistical model raises a random error or when the model is excessively complex, Machine Learning helps in solving those complexities.
So 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 2020 Machine Learning interview questions. Every interview is different and the scope of a job 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.
Below are the 24 important 2020 Machine Learning Interview Questions and Answers. These questions are divided into two parts are as follows:
- Part 1 – Machine Learning Interview Questions (Basic)
- Part 2 – Machine Learning Interview Questions (Advanced)
Part 1 – Machine Learning Interview Questions (Basic)
This first part covers the basic Interview Questions And Answers.
1. What do you understand by Machine Learning?
Answer:
Machine learning is an application of artificial intelligence that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Machine learning focuses on the development of computer programs that can access data and use it to learn for themselves.
2. 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 the task based on data they gather from sensors. They learn from the data and behave intelligently.
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3. What are the different Algorithms 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
4. What is the difference between supervised and unsupervised machine learning?
Answer:
This is the basic Machine Learning Interview Questions asked in an interview. Supervised learning is a process where it requires training labeled data While Unsupervised learning it doesn’t require data labeling.
5. What is the function of Unsupervised Learning?
Answer:
The function of Unsupervised Learning is as below:
- Find clusters of the data of the data
- Find low-dimensional representations of the data
- Find interesting directions in data
- Interesting coordinates and correlations
- Find novel observations
6. What is the function of Supervised Learning?
Answer:
The function of Supervised Learning are as below:
- Classifications
- Speech recognition
- Regression
- Predict the time series
- Annotate strings
7. What are the advantages of Naive Bayes?
Answer:
The advantages of Naive Bayes are:
- The classifier will converge quicker than discriminative models
- It cannot learn the interactions between features
8. What are the disadvantages of Naive Bayes?
Answer:
The disadvantages of Naive Bayes are:
- It is because the problem arises for continuous features
- It makes a very strong assumption on the shape of your data distribution
- It can also happen because of data scarcity
9. Why is naive Bayes so naive?
Answer:
Naive Bayes is so naive because it assumes that all of the features in a dataset are equally important and independent.
10. What is Overfitting in Machine Learning?
Answer:
This is the popular Machine Learning Interview Questions asked in an interview. Overfitting in Machine Learning is defined as when a statistical model describes random error or noise instead of the underlying relationship or when a model is excessively complex.
11. What are the conditions when Overfitting happens?
Answer:
One of the important reasons and the possibility of overfitting is because the criteria used for training the model is not the same as the criteria used to judge the efficacy of a model.
12. How can you avoid overfitting?
Answer:
We can avoid overfitting by using:
- Lots of data
- Cross-validation
Part 2 – Machine Learning Interview Questions (Advanced)
Let us now have a look at the advanced Interview Questions.
13. 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
14. 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
Let us move to the next Machine Learning Interview Questions.
15. What are parametric models and Non-Parametric models?
Answer:
Parametric models are those with a finite number of parameters and to predict new data, you only need to know the parameters of the model.
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.
16. What are the three stages to build the hypotheses or models in machine learning?
Answer:
This is the frequently asked Machine Learning Interview Questions in an interview. The three stages to build the hypotheses or model in machine learning are:
1. Model building
2. Model testing
3. Applying the model
17. What is Inductive Logic Programming in Machine Learning (ILP)?
Answer:
Inductive Logic Programming (ILP) is a subfield of machine learning which uses logic programming representing background knowledge and examples.
18. What is the difference between classification and regression?
Answer:
The difference between classification and regression are as follows:
- 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 the belonging to a class whereas regression predicts the value from a continuous set
- Classification technique is preferred over regression when the results of the model need to return the belongingness of data points in a dataset with specific explicit categories
19. What is the difference between inductive machine learning and deductive machine learning?
Answer:
The difference between inductive machine learning and deductive machine learning are as follows:
machine-learning where the model learns by examples from a set of observed instances to draw a generalized conclusion whereas in deductive learning the model first draws the conclusion and then the conclusion is drawn.
20. 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
21. 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.
22. What are the advantages of neural networks?
Answer:
This is 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.
23. What are the disadvantages of neural networks?
Answer:
Neural Network requires a large amount of training data to converge. It’s also difficult to pick the right architecture, and the internal “hidden” layers are incomprehensible.
24. What is the difference between L1 and L2 regularization?
Answer:
The difference between L1 and L2 regularization are as follows:
- L1/Laplace tends to tolerate both large values as well as very small values of coefficients more than L2/Gaussian
- L1 can yield sparse models while L2 doesn’t
- L1 and L2 regularization prevents overfitting by shrinking on the coefficients
- L2 (Ridge) shrinks all the coefficient by the same proportions but eliminates none, while L1 (Lasso) can shrink some coefficients to zero, performing variable selection
- L1 is the first-moment norm |x1-x2| that is simply the absolute dıstance between two points where L2 is 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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