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Machine Learning Interview Questions[ML]

Here are the top Machine Learning Interview Questions and Answers, along with Top Machine Learning Interview Programming Questions; employers often ask these questions. Machine learning questions can be asked on any topic related to Machine learning.<br>There are two main types of machine learning: unsupervised learning and supervised learning. It may seem that the first concept refers to prediction with human intervention whereas the second does not, but these concepts are more related to what we want to do with the data.<br>For more information about our Machine learning question, please call the

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Machine Learning Interview Questions[ML]

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  1. Machine Learning Interview Questions[ML] LEARN NOW 70-70-90-50-90 www.ducatindia.com

  2. Q1). Define the term “Machine Learning”? Ans-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? Ans-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? Ans-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? Ans-It is a supervised Machine Learning algorithm that is used 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 do you mean by cross-validation in machine learning? Ans-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. This method of sampling is done to split the dataset into smaller parts with the same number of rows, from which a random part is chosen as a test set and the rest of the parts are stored as train sets. It consists of the following techniques: Holdout method K-fold cross-validation Stratified k-fold cross-validation Leave p-out cross-validation READ MORE

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