This page represents a list of questions which can be used for preparation of machine learning interviews. Here is the link to first set of machine learning interview questions as part of this series. The following are some of the areas covered in this set of questions:
- Univariate vs Multivariate linear regression
- Supervised vs unsupervised learning
- Algorithms such as KNN, K-means, SVM etc.
Which of the following is used to represent linear regression with one variable?
Which of the following type of cost function is used for univariate linear expression?
The hypothesis function such as f(x) = mx + c where x is an variable and c is a constant, is an example of which of the following?
The hypothesis function such as f(x) = m1x1 + m2x2 + c where x1, and x2 are variables and c is a constant, is an example of which of the following?
For gradient descent to converge quickly, it is recommended to make sure features are on similar scale
PCA, KPCA, ICA are related with which of the following?
SVM can only be used to solve classification problems?
Perceptron in machine learning is used for _______?
Logistic regression models are examples of which of the following?
KNN is _________ algorithm?
K-means is _________ algorithm?
Ajitesh Kumar
Ajitesh has been recently working in the area of AI and machine learning. Currently, his research area includes Safe & Quality AI. In addition, he is also passionate about various different technologies including programming languages such as Java/JEE, Javascript and technologies such as Blockchain, mobile computing, cloud-native technologies, application security, cloud computing platforms, big data etc.
He has also authored the book, Building Web Apps with Spring 5 and Angular.
He has also authored the book, Building Web Apps with Spring 5 and Angular.
Latest posts by Ajitesh Kumar (see all)
- When not to use F-Statistics for Multi-linear Regression - July 16, 2019
- R-Squared Explained for Indian Grandma - July 14, 2019
- Machine Learning – Cloud-native Model Deployments - June 29, 2019
