Top 20 machine learning interview questions
Machine learning (ML) is the process of training a computer-related program that helps to create a statistical model based on data. It automatically learns programs from data. Machine learning is one of the by-products of artificial intelligence (AI).
Nowadays, almost 80% of enterprises already adopt machine learning and artificial intelligence and have gained enormous financial advantages from it.
So, let us quickly look into these top 20 interview questions with answers which may help you to crack your interview.
1. What is Machine learning?
It is a branch of computer science that deals with system programming to automatically take data information from the system and then execute them on its own. It also doesn't require any specific programming. It develops computer programs for accessing data and utilizes them for self-learning.
2. What are the three different types of machine learning?
Supervised learning (Task Driven) : Takes relevant decisions based on given data.
Unsupervised learning (Data-Driven) : In this, the machine itself identifies patterns and discrepancies in the input data. It doesn't need to access given data.
Reinforcement learning (Environment Driven) : It allows machines to learn from the information they received for earlier actions.
3. What is overfitting in Machine learning and how can we avoid it?
When a model gives any random error or noise instead of an underlying result then 'overfitting' occurs. Or when our model is excessively complex, overfitting is generally observed, because of having too many parameters for the number of training data types.
It can be avoided mainly by 2 ways.
Simplification : In this, we make a simple model. The variance may be lessened if we use lesser variables as well as parameters.
Regularization : It has a cost term for features that are involved with objective functions.
4. What is inductive machine learning?
It involves the process of learning by examples, where a system tries to create a general rule from a set of observed instances.
5. What are the algorithms of Machine Learning?
There are various types of algorithms but five of them are the most popular algorithms
Neural Networks (backpropagation)
Support vector machines
6. What are the different Algorithm techniques in Machine Learning?
There are six different types of techniques-
Learning to Learn
7. How can we build hypotheses or models in machine learning?
There are three stages to build the hypotheses or model in machine learning and that is as follows :
Applying the model
8. Define the standard approach of supervised learning?
In supervised learning for the standard approach, split the set of examples into the training set and the test.
9. Name various approaches for machine learning?
The various approaches are:
Concept Vs Classification Learning
Symbolic Vs Statistical Learning
Inductive Vs Analytical Learning
10. Explain the function of 'Supervised Learning'?
Predict time series
11. Explain what is the function of 'Unsupervised Learning'?
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
12. How pattern recognition is used in machine learning?
It can be used in various ways
13. Which method is used to prevent overfitting?
When there is sufficient data, 'Isotonic Regression' is used to prevent an overfitting issue.
14. Explain the two classification methods that SVM (Support Vector Machine) can handle?
Combining binary classifiers
Modifying binary to incorporate multiclass learning
15. What is the full form and use of PCA, KPCA, and ICA?
PCA (Principal Components Analysis)
KPCA- (Kernel-based Principal Component Analysis)
ICA- (Independent Component Analysis)
These are extraction techniques mainly used for dimensionality reduction.
16. What are sequence learning and its types?
It is a method of teaching and learning in a logical way.
17. What are support vector machines?
These are supervised learning algorithms used for classification and regression analysis.
18. Explain dimension reduction in machine learning?
As its name suggests it is a process of reducing the number of random variables under consideration and can be divided into feature selection and feature extraction.
19. What is Perceptron in Machine Learning?
Perceptron is a supervised learning method for binary classifiers where a binary classifier is a deciding function of whether an input represents a vector or a number.
20. What is ensemble learning?
To solve a particular computational program, multiple models such as classifiers or experts are strategically generated and combined.