Machine learning Interview Questions

All Interview Company Interview Technical Interview Web Interview PHP Interview .Net Interview Java Interview Database Interview Machine Learning Interview Questions 1 What do you understand by Machine learning 2 Differentiate between inductive learning and deductive learning 3 What is the difference between Data Mining and Machine Learning 4 What is the meaning of Overfitting in Machine learning 5 Why overfitting occurs 6 What is the method to avoid overfitting 7 Differentiate supervised and unsupervised machine learning. 8 How does Machine Learning differ from Deep Learning 9 How is KNN different from kmeans 10 What are the different types of Algorithm methods in Machine Learning 11 What do you understand by Reinforcement Learning technique 12 What is the tradeoff between bias and variance 13 How do classification and regression differ 14 What are the five popular algorithms we use in Machine Learning 15 What do you mean by ensemble learning 16 What is a model selection in Machine Learning 17 What are the three stages of building the hypotheses or model in machine learning 18 What according to you, is the standard approach to supervised learning 19 Describe Training set and training Test. 20 What are the common ways to handle missing data in a dataset 21 What do you understand by ILP 22 What are the necessary steps involved in Machine Learning Project 23 Describe Precision and Recall 24 What do you understand by Decision Tree in Machine Learning 25 What are the functions of Supervised Learning 26 What are the functions of Unsupervised Learning 27 What do you understand by algorithm independent machine learning 28 Describe the classifier in machine learning. 29 What do you mean by Genetic Programming 30 What is SVM in machine learning What are the classification methods that SVM can handle 31 How will you explain a linked list and an array 32 What do you understand by the Confusion Matrix 33 Explain True Positive, True Negative, False Positive, and False Negative in Confusion Matrix with an example. 34 What according to you, is more important between model accuracy and model performance 35 What is Bagging and Boosting 36 What are the similarities and differences between bagging and boosting in Machine Learning 37 What do you understand by Cluster Sampling 38 What do you know about Bayesian Networks 39 Which are the two components of Bayesian logic program 40 Describe dimension reduction in machine learning. 41 Why instancebased learning algorithm sometimes referred to as Lazy learning algorithm 42 What do you understand by the F1 score 43 How is a decision tree pruned 44 What are the Recommended Systems 45 What do you understand by Underfitting 46 When does regularization become necessary in Machine Learning 47 What is Regularization What kind of problems does regularization solve 48 Why do we need to convert categorical variables into factor Which functions are used to perform the conversion 49 Do you think that treating a categorical variable as a continuous variable would result in a better predictive model 50 How is machine learning used in daytoday life Javatpoint Services Training For College Campus Learn Tutorials Our Websites Our Services Contact

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Data Science Top Machine Learning Interview Questions You Must Prepare In 2021 Machine Learning Interview Questions and Answers Machine Learning Interview Preparation Edureka Machine Learning Core Interview Question Q1. What are the different types of Machine Learning Q2. How would you explain Machine Learning to a schoolgoing kid Q3.How does Deep Learning differfrom Machine Learning Q4.Explain Classification and Regression Q5.What do you understand by selection bias Q6.What do you understand by Precision and Recall Q7.Explain false negative, false positive, true negative and true positive with a simple example. Q8.What is aConfusion Matrix Q9.What is the difference between inductive and deductive learning Q10.How is KNN different from Kmeans clustering Q11.What is ROC curve and what does it represent Q12.Whats the difference between Type I and Type II error Q14.Which is more important to you model accuracy or model performance Q15.What is the difference between Gini Impurity and Entropy in a Decision Tree Q16.What is the difference between Entropy and Information Gain Q17.What is Overfitting And how do you ensure youre not overfitting with a model Q18.Explain Ensemble learning technique in Machine Learning. Q19.What is bagging and boosting in Machine Learning Q20.How would you screen for outliers and what should you do if you find one Q21.What are collinearity and multicollinearity Q22.What do you understand by Eigenvectors and Eigenvalues Q23.What is AB Testing Q24.What is Cluster Sampling Q25.Running a binary classification tree algorithm is quite easy. But do you know how the tree decides on which variable to split at the root node and its succeeding child nodes Machine Learning With Python Questions Q1.Name a few libraries in Python used for Data Analysis and Scientific Computations. Q2.Which library would you prefer for plotting in Python language Seaborn or Matplotlib or Bokeh Q3.How are NumPy and SciPy related Q4.What is the main difference between a Pandas series and a singlecolumn DataFrame in Python Q5.How can you handle duplicate values in a dataset for a variable in Python Q6.Write a basic Machine Learning program to check the accuracy of a model, by importing any dataset using any classifier Machine Learning Scenario Based Questions Q1.You are given a data set consisting of variables having more than 30 missing values Lets say, out of 50 variables, 8 variables have missing values higher than 30. How will you deal with them Q2.Write an SQL query that makes recommendations using the pages that your friends liked. Assume you have two tables a twocolumn table of users and their friends, and a twocolumn table of users and the pages they liked. It should not recommend pages you already like. Q3.Theres a game where you are asked to roll two fair sixsided dice. If the sum of the values on the dice equals seven, then you win 21. However, you must pay 5 to play each time you roll both dice. Do you play this game And in the followup If he plays 6 times what is the probability of making money from this game Q4.We have two options for serving ads within Newsfeed 1 out of every 25 stories, one will be an ad 2 every story has a 4 chance of being an ad For each option, what is the expected number of ads shown in 100 news stories If we go with option 2, what is the chance a user will be shown only a single ad in 100 stories What about no ads at all Q5.How would you predict who will renew their subscription next month What data would you need to solve this What analysis would you do Would you build predictive models If so, which algorithms Q6.How do you map nicknames Pete, Andy, Nick, Rob, etc to real names Q7. A jar has 1000 coins, of which 999 are fair and 1 is double headed. Pick a coin at random, and toss it 10 times. Given that you see 10 heads, what is the probability that the next toss of that coin is also a head Q8.Suppose you are given a data set which has missing values spread along 1 standard deviation from the median. What percentage of data would remain unaffected and Why Q9.You are given a cancer detection data set. Lets suppose when you build a classification model you achieved an accuracy of 96. Why shouldnt you be happy with your model performance What can you do about it Q10.You are working on a time series data set. Your manager has asked you to build a high accuracy model. You start with the decision tree algorithm since you know it works fairly well on all kinds of data. Later, you tried a time series regression model and got higher accuracy than the decision tree model. Can this happen Why Q11.Suppose you found that your model is suffering from low bias and high variance. Which algorithm you think could tackle this situation and Why Q12.You are given a data set. The data set contains many variables, some of which are highly correlated and you know about it. Your manager has asked you to run PCA. Would you remove correlated variables first Why Q13.You are asked to build a multiple regression model but your model R isnt as good as you wanted. For improvement, you remove the intercept term now your model R becomes 0.8 from 0.3. Is it possible How Q14.Youre asked to build a random forest model with 10000 trees. During its training, you got training error as 0.00. But, on testing the validation error was 34.23. What is going on Havent you trained your model perfectly Recommended videos for you Machine Learning with Python Python Numpy Tutorial Arrays In Python Python Tutorial All You Need To Know In Python Programming Application of Clustering in Data Science Using RealTime Examples The Whys and Hows of Predictive ModelingII Data Science Make Smarter Business Decisions Diversity Of Python Programming Python Programming Learn Python Programming From Scratch Web Scraping And Analytics With Python Python Loops While, For and Nested Loops in Python Programming 3 Scenarios Where Predictive Analytics is a Must Introduction to Business Analytics with R Python List, Tuple, String, Set And Dictonary Python Sequences Know The Science Behind Product Recommendation With R Programming Android Development Using Android 5.0 Lollipop Business Analytics Decision Tree in R Mastering Python An Excellent tool for Web Scraping and Data Analysis Business Analytics with R Linear Regression With R The Whys and Hows of Predictive ModellingI Recommended blogs for you How to Implement Matrices in Python using NumPy What are the Best Books for Data Science Predictive Analytics Process in Business Analytics with R Understanding Range Function and Sequences in Python How to Convert a String to integer using Python Object Oriented Programming Python All you need to know Machine Learning Career and Future Scope A Comprehensive Guide To R For Data Science A Complete Guide To Math And Statistics For Data Science Understanding Logistic Regression in R What is print in Python and How to use its Parameters Introduction To Python All You Need To know About Python Google Data Science Interview Questions All you need to know to crack It What is the Main Function in Python and how to use it Learn How To Use Split Function In Python String Trimming in Python All you Need to Know How to implement Time Sleep in Python All You Need to Know About Eval in Python Why Should you go for Python A Step By Step Guide To Linear Regression In R Join the discussionCancel reply Trending Courses in Data Science Data Science Certification Training with Pyth ... 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Data Science Top Machine Learning Interview Questions You Must Prepare In 2021 Machine Learning Interview Questions and Answers Machine Learning Interview Preparation Edureka Machine Learning Core Interview Question Q1. What are the different types of Machine Learning Q2. How would you explain Machine Learning to a schoolgoing kid Q3.How does Deep Learning differfrom Machine Learning Q4.Explain Classification and Regression Q5.What do you understand by selection bias Q6.What do you understand by Precision and Recall Q7.Explain false negative, false positive, true negative and true positive with a simple example. Q8.What is aConfusion Matrix Q9.What is the difference between inductive and deductive learning Q10.How is KNN different from Kmeans clustering Q11.What is ROC curve and what does it represent Q12.Whats the difference between Type I and Type II error Q14.Which is more important to you model accuracy or model performance Q15.What is the difference between Gini Impurity and Entropy in a Decision Tree Q16.What is the difference between Entropy and Information Gain Q17.What is Overfitting And how do you ensure youre not overfitting with a model Q18.Explain Ensemble learning technique in Machine Learning. Q19.What is bagging and boosting in Machine Learning Q20.How would you screen for outliers and what should you do if you find one Q21.What are collinearity and multicollinearity Q22.What do you understand by Eigenvectors and Eigenvalues Q23.What is AB Testing Q24.What is Cluster Sampling Q25.Running a binary classification tree algorithm is quite easy. But do you know how the tree decides on which variable to split at the root node and its succeeding child nodes Machine Learning With Python Questions Q1.Name a few libraries in Python used for Data Analysis and Scientific Computations. Q2.Which library would you prefer for plotting in Python language Seaborn or Matplotlib or Bokeh Q3.How are NumPy and SciPy related Q4.What is the main difference between a Pandas series and a singlecolumn DataFrame in Python Q5.How can you handle duplicate values in a dataset for a variable in Python Q6.Write a basic Machine Learning program to check the accuracy of a model, by importing any dataset using any classifier Machine Learning Scenario Based Questions Q1.You are given a data set consisting of variables having more than 30 missing values Lets say, out of 50 variables, 8 variables have missing values higher than 30. How will you deal with them Q2.Write an SQL query that makes recommendations using the pages that your friends liked. Assume you have two tables a twocolumn table of users and their friends, and a twocolumn table of users and the pages they liked. It should not recommend pages you already like. Q3.Theres a game where you are asked to roll two fair sixsided dice. If the sum of the values on the dice equals seven, then you win 21. However, you must pay 5 to play each time you roll both dice. Do you play this game And in the followup If he plays 6 times what is the probability of making money from this game Q4.We have two options for serving ads within Newsfeed 1 out of every 25 stories, one will be an ad 2 every story has a 4 chance of being an ad For each option, what is the expected number of ads shown in 100 news stories If we go with option 2, what is the chance a user will be shown only a single ad in 100 stories What about no ads at all Q5.How would you predict who will renew their subscription next month What data would you need to solve this What analysis would you do Would you build predictive models If so, which algorithms Q6.How do you map nicknames Pete, Andy, Nick, Rob, etc to real names Q7. A jar has 1000 coins, of which 999 are fair and 1 is double headed. Pick a coin at random, and toss it 10 times. Given that you see 10 heads, what is the probability that the next toss of that coin is also a head Q8.Suppose you are given a data set which has missing values spread along 1 standard deviation from the median. What percentage of data would remain unaffected and Why Q9.You are given a cancer detection data set. Lets suppose when you build a classification model you achieved an accuracy of 96. Why shouldnt you be happy with your model performance What can you do about it Q10.You are working on a time series data set. Your manager has asked you to build a high accuracy model. You start with the decision tree algorithm since you know it works fairly well on all kinds of data. Later, you tried a time series regression model and got higher accuracy than the decision tree model. Can this happen Why Q11.Suppose you found that your model is suffering from low bias and high variance. Which algorithm you think could tackle this situation and Why Q12.You are given a data set. The data set contains many variables, some of which are highly correlated and you know about it. Your manager has asked you to run PCA. Would you remove correlated variables first Why Q13.You are asked to build a multiple regression model but your model R isnt as good as you wanted. For improvement, you remove the intercept term now your model R becomes 0.8 from 0.3. Is it possible How Q14.Youre asked to build a random forest model with 10000 trees. During its training, you got training error as 0.00. But, on testing the validation error was 34.23. What is going on Havent you trained your model perfectly Recommended videos for you Machine Learning with Python Python Numpy Tutorial Arrays In Python Python Tutorial All You Need To Know In Python Programming Application of Clustering in Data Science Using RealTime Examples The Whys and Hows of Predictive ModelingII Data Science Make Smarter Business Decisions Diversity Of Python Programming Python Programming Learn Python Programming From Scratch Web Scraping And Analytics With Python Python Loops While, For and Nested Loops in Python Programming 3 Scenarios Where Predictive Analytics is a Must Introduction to Business Analytics with R Python List, Tuple, String, Set And Dictonary Python Sequences Know The Science Behind Product Recommendation With R Programming Android Development Using Android 5.0 Lollipop Business Analytics Decision Tree in R Mastering Python An Excellent tool for Web Scraping and Data Analysis Business Analytics with R Linear Regression With R The Whys and Hows of Predictive ModellingI Recommended blogs for you How to Implement Matrices in Python using NumPy What are the Best Books for Data Science Predictive Analytics Process in Business Analytics with R Understanding Range Function and Sequences in Python How to Convert a String to integer using Python Object Oriented Programming Python All you need to know Machine Learning Career and Future Scope A Comprehensive Guide To R For Data Science A Complete Guide To Math And Statistics For Data Science Understanding Logistic Regression in R What is print in Python and How to use its Parameters Introduction To Python All You Need To know About Python Google Data Science Interview Questions All you need to know to crack It What is the Main Function in Python and how to use it Learn How To Use Split Function In Python String Trimming in Python All you Need to Know How to implement Time Sleep in Python All You Need to Know About Eval in Python Why Should you go for Python A Step By Step Guide To Linear Regression In R Join the discussionCancel reply Trending Courses in Data Science Data Science Certification Training with Pyth ... Python Programming Certification Training Machine Learning Certification Training Data Science Certification Course using R Data Analytics with R Certification Training Statistics Essentials for Analytics Advanced Predictive Modelling in R Certificat ... SAS Training and Certification Analytics for Retail Banks Decision Tree Modeling Using R Certification ... Browse Categories Subscribe to our Newsletter, and get personalized recommendations.

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All Interview Company Interview Technical Interview Web Interview PHP Interview .Net Interview Java Interview Database Interview Machine Learning Interview Questions 1 What do you understand by Machine learning 2 Differentiate between inductive learning and deductive learning 3 What is the difference between Data Mining and Machine Learning 4 What is the meaning of Overfitting in Machine learning 5 Why overfitting occurs 6 What is the method to avoid overfitting 7 Differentiate supervised and unsupervised machine learning. 8 How does Machine Learning differ from Deep Learning 9 How is KNN different from kmeans 10 What are the different types of Algorithm methods in Machine Learning 11 What do you understand by Reinforcement Learning technique 12 What is the tradeoff between bias and variance 13 How do classification and regression differ 14 What are the five popular algorithms we use in Machine Learning 15 What do you mean by ensemble learning 16 What is a model selection in Machine Learning 17 What are the three stages of building the hypotheses or model in machine learning 18 What according to you, is the standard approach to supervised learning 19 Describe Training set and training Test. 20 What are the common ways to handle missing data in a dataset 21 What do you understand by ILP 22 What are the necessary steps involved in Machine Learning Project 23 Describe Precision and Recall 24 What do you understand by Decision Tree in Machine Learning 25 What are the functions of Supervised Learning 26 What are the functions of Unsupervised Learning 27 What do you understand by algorithm independent machine learning 28 Describe the classifier in machine learning. 29 What do you mean by Genetic Programming 30 What is SVM in machine learning What are the classification methods that SVM can handle 31 How will you explain a linked list and an array 32 What do you understand by the Confusion Matrix 33 Explain True Positive, True Negative, False Positive, and False Negative in Confusion Matrix with an example. 34 What according to you, is more important between model accuracy and model performance 35 What is Bagging and Boosting 36 What are the similarities and differences between bagging and boosting in Machine Learning 37 What do you understand by Cluster Sampling 38 What do you know about Bayesian Networks 39 Which are the two components of Bayesian logic program 40 Describe dimension reduction in machine learning. 41 Why instancebased learning algorithm sometimes referred to as Lazy learning algorithm 42 What do you understand by the F1 score 43 How is a decision tree pruned 44 What are the Recommended Systems 45 What do you understand by Underfitting 46 When does regularization become necessary in Machine Learning 47 What is Regularization What kind of problems does regularization solve 48 Why do we need to convert categorical variables into factor Which functions are used to perform the conversion 49 Do you think that treating a categorical variable as a continuous variable would result in a better predictive model 50 How is machine learning used in daytoday life Javatpoint Services Training For College Campus Learn Tutorials Our Websites Our Services Contact

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Tutorial Playlist Top 34 Machine Learning Interview Questions and Answers 2021 Top Machine Learning Interview Questions 1. What Are the Different Types of Machine Learning 2. What is Overfitting, and How Can You Avoid It 3. What is training Set and test Set in a Machine Learning Model How Much Data Will You Allocate for Your Training, Validation, and Test Sets 4. How Do You Handle Missing or Corrupted Data in a Dataset 5. How Can You Choose a Classifier Based on a Training Set Data Size 6. Explain the Confusion Matrix with Respect to Machine Learning Algorithms. 7. What Is a False Positive and False Negative and How Are They Significant 8. What Are the Three Stages of Building a Model in Machine Learning 9. What is Deep Learning 10. What Are the Differences Between Machine Learning and Deep Learning 11. What Are the Applications of Supervised Machine Learning in Modern Businesses 12. What is Semisupervised Machine Learning 13. What Are Unsupervised Machine Learning Techniques 14. What is the Difference Between Supervised and Unsupervised Machine Learning 15. What is the Difference Between Inductive Machine Learning and Deductive Machine Learning 16. Compare Kmeans and KNN Algorithms. 17. What Is naive in the Naive Bayes Classifier 18. Explain How a System Can Play a Game of Chess Using Reinforcement Learning. 19. How Will You Know Which Machine Learning Algorithm to Choose for Your Classification Problem 20. How is Amazon Able to Recommend Other Things to Buy How Does the Recommendation Engine Work 21. When Will You Use Classification over Regression 22. How Do You Design an Email Spam Filter 23. What is a Random Forest 24. Considering a Long List of Machine Learning Algorithms, given a Data Set, How Do You Decide Which One to Use 25. What is Bias and Variance in a Machine Learning Model 26. What is the Tradeoff Between Bias and Variance 27. Define Precision and Recall. 28. What is a Decision Tree Classification 29. What is Pruning in Decision Trees, and How Is It Done 30. Briefly Explain Logistic Regression. 31. Explain the K Nearest Neighbor Algorithm. 32. What is a Recommendation System 33. What is Kernel SVM 34. What Are Some Methods of Reducing Dimensionality Become Part of the Machine Learning Talent Pool Find our Post Graduate Program in AI and Machine Learning Online Bootcamp in top cities About the Author Recommended Programs Recommended Resources

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Machine Learning Interview Questions and Answers for 2021

Machine Learning Interview Questions and Answers for 2021 Machine Learning Interview Questions and Answers for 2021 Why pursue a machine learning engineer job Machine Learning Interview Questions and ...

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16 Machine Learning Interview Questions and Answers for EntryLevel Applicants 16 Machine Learning Interview Questions 1. What is data normalization and why do we need it 2. Explain dimensionality redu...

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Machine Learning Interview Questions and Answers for 2021

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Machine Learning Interview questions Interviews are one of the most important parts of a job and that too in a Technical Computer Science Interview. In India, Algorithm and Theory based Machine Learning interview questions Q. What are the different types of Machine Learning Algorithms Q. What is overfitting and how can you avoid it Q. What is the difference between classification and regression in Machine Learning Q. What is Training set and Test set in Machine Learning Q. What is Linear Regression Q. What are Bias and Variance Q. What is the difference between inductive and deductive learning Q. What is Variance Inflation Factor Q. How do you handle missing data or corrupted data in the dataset Q. Explain the Confusion Matrix with Respect to Machine Learning Algorithms. Q. Compare Kmeans and KNN algorithms. Q. What is ROC curve What does it represent Q. What is the difference between type I and type II error Q. What are collinearity and multicollinearity Q. What Is a Random Forest Q. When Will You Use Classification over Regression Q. What are Eigenvectors and Eigenvalues Q. What is SVM Support Vector Machines Q. Implement the KNN classification algorithm. Q. What is cluster sampling Q. How will you design an Email spam filter Q. How does the recommendation engine work on ecommerce websites Q. How can you help our marketing team be more efficient Q. Youve built a random forest model with 10000 trees. You got delighted after getting a training error of 0.00. But, the validation error is 34.23. What is going on Havent you trained your model perfectly Q. Comment on the statement. Treating a categorical variable as a continuous variable would result in a better predictive model Q. You are given a data set consisting of variables having more than 20 missing values Lets say, out of 50 variables, 8 variables have missing values higher than 20. How will you deal with them Q. How would you approach the Netflix Prize competition Q. Explain How a System Can Play a Game of Chess Using Reinforcement Learning. Q. How Will You decide which Machine Learning Algorithm to choose for your classification problem Q. How will you implement Facebooks people you may know using machine learning Q. How machine learning powers targeted advertising Q. Give a brief overview of sentiment analysis using machine learning. Q. How many trigrams phrases can be generated from the given sentence, after performing the following text cleaning steps 1.Stopword Removal 2.Replacing punctuations by a single space Verzeo is a great source to learn datascience. Q. How would you implement a recommendation system for our companys users Q. How do you think Google is training data for selfdriving cars

Machine learning Interview Questions

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