Top 10 Most Important Interview Question and Answer on Machine Learning

1. MACHINE LEARNING INTERVIEW QUESTIONS 070709 05090 070709 05090 https://tutorials.ducatindia.com

2. Q1). Define the term “Machine Learning”. It is defined as a subset of artificial intelligence (AI) technology which allow systems to learn and develop from experience automatically without being programmed specifically. The focus of machine learning is on designing computer programmes which can access and use data to learn for themselves.

3. Q2). Differentiate between supervised and unsupervised machine learning? Supervised learning requires labelled training dataset. For instance, to train the model, firstly it needs to be classified dataset and then label into labelled groups. On the other side, unsupervised learning does not need any labelling data explicitly.

4. Q3). Name the phases of the life cycle of machine learning. The phases of the life cycle are as follows: Data gathering Data preparation Data wrangling Data analysis Data selection and verification Data deployment

5. Q4). What is a Linear Regression? It is a supervised Machine Learning algorithm that is use for predictive analysis to find the linear relationship between the dependent and the independent variables. The linear Regression equation is: y=mX +c Where y= Dependent variable x = Independent variable m = Coefficient of X c = Intercept point

6. Q5). What are the types of Machine Learning? There are Three types of Machine Learning. They are given below. Supervised Learning Unsupervised Learning Reinforcement Learning

7. Q6). Differentiate between classification and regression in Machine Learning. There are different kinds of prediction problems in machine learning that are based on supervised and unsupervised learning. There are classification, clustering and association. Here, we are going to explore classification and regression. Classification Regression

8. Q7). What is model selection in Machine Learning? Model selection is defined as the process of selecting models from various mathematical models that are used to define the same data set. The selection of models is applied to statistics, machine learning and data mining fields.

9. Q8). Name the three stages which are required to build the hypotheses or model in machine learning. The three stages which are required to build the hypotheses or model in machine learning are as follows: Building the model Testing the model Implementing the model

10. Q9). What do you mean by cross-validation in machine learning? In Machine Learning, the cross- validation method enables a framework to improve the efficiency of the given Machine Learning algorithm to which you feed multiple sample data from the dataset. It consists of the following techniques: Holdout method K-fold cross-validation Stratified k-fold cross-validation Leave p-out cross-validation

11. Q10). Explain logistic regression in detail. The proper regression analysis used when the dependent variable is categorical or binary is logistic regression. Logistic regression is a tool for predictive analysis, like other regression analyses. To describe information and the relationship between one dependent binary variable and one or more independent variables, logistic regression is used. Also, it is used to estimate the likelihood of a categorical dependent variable.

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