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How to explain a Data Science project in an interview?
Today's Interview QnAs
Company Name - Deloitte
Role - Data Scientist
--------------------------------------
Q1. Difference between Correlation and Regression.
Ans. The main difference in correlation vs regression is that the measures of the degree of a relationship between two variables; let them be x and y. Here, correlation is for the measurement of degree, whereas regression is a parameter to determine how one variable affects another.
Q2. Why do we square the residuals instead of using modulus?
Ans. It is because of the extra penalty for higher errors and squaring the residuals for mean deviation were observed to be more efficient than mean absolute deviation.
Q3. Which evaluation metric should you prefer to use for a dataset having a lot of outliers in it?
Ans. Mean Absolute Error(MAE) is preferred when we have too many outliers present in the dataset because MAE is robust to outliers whereas MSE and RMSE are very susceptible to outliers and these start penalizing the outliers by squaring the error terms.
Q4. Heteroscedasticity? How to detect it?
Ans. Heteroskedasticity refers to situations where the variance of the residuals is unequal over a range of measured values. When running a regression analysis, heteroskedasticity results in an unequal scatter of the residuals (also known as the error term). To check for heteroscedasticity, you need to assess the residuals by fitted value plots specifically.
Q5. p-value?
Ans. A p-value is a measure of the probability that an observed difference could have occurred just by random chance. The lower the p-value, the greater the statistical significance of the observed difference. P-value can be used as an alternative to or in addition to pre-selected confidence levels for hypothesis testing.
Q6. Root Cause Analysis?
Ans. Root cause analysis (RCA) is defined as a collective term that describes a wide range of approaches used to uncover causes of problems. Some RCA approaches are geared more toward identifying true root causes than others.
Q7. Regularization?
Ans. Regularization is the process which regularizes or shrinks the coefficients towards zero. In simple words, regularization discourages learning a more complex or flexible model, to prevent overfitting.
Q8. DBSCAN Clustering?
Ans. Density-based spatial clustering of applications with noise (DBSCAN) is a well-known data clustering algorithm that is commonly used in data mining and machine learning. The main idea behind DBSCAN is that a point belongs to a cluster if it is close to many points from that cluster. It is able to find arbitrary shaped clusters and clusters with noise (i.e. outliers).
--------------------------------------
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Company Name - Deloitte
Role - Data Scientist
--------------------------------------
Q1. Difference between Correlation and Regression.
Ans. The main difference in correlation vs regression is that the measures of the degree of a relationship between two variables; let them be x and y. Here, correlation is for the measurement of degree, whereas regression is a parameter to determine how one variable affects another.
Q2. Why do we square the residuals instead of using modulus?
Ans. It is because of the extra penalty for higher errors and squaring the residuals for mean deviation were observed to be more efficient than mean absolute deviation.
Q3. Which evaluation metric should you prefer to use for a dataset having a lot of outliers in it?
Ans. Mean Absolute Error(MAE) is preferred when we have too many outliers present in the dataset because MAE is robust to outliers whereas MSE and RMSE are very susceptible to outliers and these start penalizing the outliers by squaring the error terms.
Q4. Heteroscedasticity? How to detect it?
Ans. Heteroskedasticity refers to situations where the variance of the residuals is unequal over a range of measured values. When running a regression analysis, heteroskedasticity results in an unequal scatter of the residuals (also known as the error term). To check for heteroscedasticity, you need to assess the residuals by fitted value plots specifically.
Q5. p-value?
Ans. A p-value is a measure of the probability that an observed difference could have occurred just by random chance. The lower the p-value, the greater the statistical significance of the observed difference. P-value can be used as an alternative to or in addition to pre-selected confidence levels for hypothesis testing.
Q6. Root Cause Analysis?
Ans. Root cause analysis (RCA) is defined as a collective term that describes a wide range of approaches used to uncover causes of problems. Some RCA approaches are geared more toward identifying true root causes than others.
Q7. Regularization?
Ans. Regularization is the process which regularizes or shrinks the coefficients towards zero. In simple words, regularization discourages learning a more complex or flexible model, to prevent overfitting.
Q8. DBSCAN Clustering?
Ans. Density-based spatial clustering of applications with noise (DBSCAN) is a well-known data clustering algorithm that is commonly used in data mining and machine learning. The main idea behind DBSCAN is that a point belongs to a cluster if it is close to many points from that cluster. It is able to find arbitrary shaped clusters and clusters with noise (i.e. outliers).
--------------------------------------
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The Most Important thing a recruiter looks in you ?
Today's Interview Quest N Ans
Company Name - L&T Financial Services
Role - Data Scientist
--------------------------------------
Q1. Assumptions in Multiple linear regression
Ans. The regression has five key assumptions:
Linear relationship.
Multivariate normality.
No or little multicollinearity.
No auto-correlation.
Homoscedasity
Q2. Entropy
Ans. Entropy is a measure of disorder or uncertainty and the goal of machine learning models and Data Scientists in general is to reduce uncertainty. Entropy can be defined as a measure of the purity of the sub split. Entropy always lies between 0 to 1.
Q3. Random forest algorithm
Ans. Random Forest is a classifier that contains a number of decision trees on various subsets of the given dataset and takes the average to improve the predictive accuracy of that dataset. It is based on model ensemble learning technique.
Q4. XGBoost Algorithm
Ans. XGBoost is an algorithm that has recently been dominating applied machine learning and Kaggle competitions for structured or tabular data. XGBoost is an implementation of gradient boosted decision trees designed for speed and performance.
Q5. Central limit theorem
Ans. The central limit theorem states that if you have a population with mean ฮผ and standard deviation ฯ and take sufficiently large random samples from the population with replacement , then the distribution of the sample means will be approximately normally distributed.
Q6. VIF
Ans. Variance Inflation Factor (VIF) is used to detect the presence of multicollinearity. Variance inflation factors (VIF) measure how much the variance of the estimated regression coefficients are inflated as compared to when the predictor variables are not linearly related.
Q7. Difference Between Bagging and Boosting
Ans. Bagging is a way to decrease the variance in the prediction by generating additional data for training from dataset using combinations with repetitions to produce multi-sets of the original data. Boosting is an iterative technique which adjusts the weight of an observation based on the last classification.
Q8. P value and itโs significance
Ans. The p-value is the probability that the null hypothesis is true. (1 โ the p-value) is the probability that the alternative hypothesis is true. A low p-value shows that the results are replicable. A low p-value shows that the effect is large or that the result is of major theoretical, clinical or practical importance.
Q9. Type I and Type II error
Ans. A type I error (false-positive) occurs if an investigator rejects a null hypothesis that is actually true in the population; a type II error (false-negative) occurs if the investigator fails to reject a null hypothesis that is actually false in the population.
--------------------------------------
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Company Name - L&T Financial Services
Role - Data Scientist
--------------------------------------
Q1. Assumptions in Multiple linear regression
Ans. The regression has five key assumptions:
Linear relationship.
Multivariate normality.
No or little multicollinearity.
No auto-correlation.
Homoscedasity
Q2. Entropy
Ans. Entropy is a measure of disorder or uncertainty and the goal of machine learning models and Data Scientists in general is to reduce uncertainty. Entropy can be defined as a measure of the purity of the sub split. Entropy always lies between 0 to 1.
Q3. Random forest algorithm
Ans. Random Forest is a classifier that contains a number of decision trees on various subsets of the given dataset and takes the average to improve the predictive accuracy of that dataset. It is based on model ensemble learning technique.
Q4. XGBoost Algorithm
Ans. XGBoost is an algorithm that has recently been dominating applied machine learning and Kaggle competitions for structured or tabular data. XGBoost is an implementation of gradient boosted decision trees designed for speed and performance.
Q5. Central limit theorem
Ans. The central limit theorem states that if you have a population with mean ฮผ and standard deviation ฯ and take sufficiently large random samples from the population with replacement , then the distribution of the sample means will be approximately normally distributed.
Q6. VIF
Ans. Variance Inflation Factor (VIF) is used to detect the presence of multicollinearity. Variance inflation factors (VIF) measure how much the variance of the estimated regression coefficients are inflated as compared to when the predictor variables are not linearly related.
Q7. Difference Between Bagging and Boosting
Ans. Bagging is a way to decrease the variance in the prediction by generating additional data for training from dataset using combinations with repetitions to produce multi-sets of the original data. Boosting is an iterative technique which adjusts the weight of an observation based on the last classification.
Q8. P value and itโs significance
Ans. The p-value is the probability that the null hypothesis is true. (1 โ the p-value) is the probability that the alternative hypothesis is true. A low p-value shows that the results are replicable. A low p-value shows that the effect is large or that the result is of major theoretical, clinical or practical importance.
Q9. Type I and Type II error
Ans. A type I error (false-positive) occurs if an investigator rejects a null hypothesis that is actually true in the population; a type II error (false-negative) occurs if the investigator fails to reject a null hypothesis that is actually false in the population.
--------------------------------------
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Please Introduce Yourself ???
#internship
CloudyML is hiring for NLP Intern.
It will be a Paid Internship
Apply Now๐๐ป https://bit.ly/Apply4internship
CloudyML is hiring for NLP Intern.
It will be a Paid Internship
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Today's Interview QnAs
Company Name - Philips
Role - Data Scientist
................................
Q1. Time Series (ARIMA)?
Ans. ARIMA, short for โAutoRegressive Integrated Moving Averageโ, is a forecasting algorithm based on the idea that the information in the past values of the time series can alone be used to predict the future values.
Q2. How to reduce overfitting ?
Ans. Techniques to reduce overfitting:
Increase training data.
*Reduce model complexity.
*Early stopping during the training phase.
*Ridge Regularization and Lasso Regularization.
Q3. What is precision/recall ratio?
Ans. When it comes to precision we're talking about the true positives over the true positives plus the false positives. As opposed to recall which is the number of true positives over the true positives and the false negatives.
Q4. Dimensionality reduction?
Ans. Dimensionality Reduction is used to reduce the feature space with consideration by a set of principal features.
Q5. Bias and variance?
Ans. Bias is one type of error which occurs due to wrong assumptions about data such as assuming data is linear when in reality, data follows a complex function. On the other hand, variance gets introduced with high sensitivity to variations in training data.
Q6. Difference between classification and clustering?
Ans. In classification data are grouped by analyzing data objects whose class label is known. Clustering analyzes data objects without knowing class label. There is some prior knowledge of attributes of each classification. There is no prior knowledge of attributes of data to form clusters.
Q7. Deal unbalanced classification?
Ans. Techniques to Handle Imbalanced Data
Use the right evaluation metrics
*Use K-fold Cross-Validation in the right way
*Ensemble different resampled datasets
*Resample with different ratios
*Cluster the abundant class
*Design your own models
........................
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๐๐ป Discounted course link in description
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Company Name - Philips
Role - Data Scientist
................................
Q1. Time Series (ARIMA)?
Ans. ARIMA, short for โAutoRegressive Integrated Moving Averageโ, is a forecasting algorithm based on the idea that the information in the past values of the time series can alone be used to predict the future values.
Q2. How to reduce overfitting ?
Ans. Techniques to reduce overfitting:
Increase training data.
*Reduce model complexity.
*Early stopping during the training phase.
*Ridge Regularization and Lasso Regularization.
Q3. What is precision/recall ratio?
Ans. When it comes to precision we're talking about the true positives over the true positives plus the false positives. As opposed to recall which is the number of true positives over the true positives and the false negatives.
Q4. Dimensionality reduction?
Ans. Dimensionality Reduction is used to reduce the feature space with consideration by a set of principal features.
Q5. Bias and variance?
Ans. Bias is one type of error which occurs due to wrong assumptions about data such as assuming data is linear when in reality, data follows a complex function. On the other hand, variance gets introduced with high sensitivity to variations in training data.
Q6. Difference between classification and clustering?
Ans. In classification data are grouped by analyzing data objects whose class label is known. Clustering analyzes data objects without knowing class label. There is some prior knowledge of attributes of each classification. There is no prior knowledge of attributes of data to form clusters.
Q7. Deal unbalanced classification?
Ans. Techniques to Handle Imbalanced Data
Use the right evaluation metrics
*Use K-fold Cross-Validation in the right way
*Ensemble different resampled datasets
*Resample with different ratios
*Cluster the abundant class
*Design your own models
........................
P.S.: If you wanna be a Pro Data Scientist and crack any Job interviews, join our Data Science OR Deep Learning Course. Get hands on experience with doubts clearance support at best affordable price.
๐๐ป Discounted course link in description
Happy Learning With CloudyML ๐
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Today's Interview QnAs
Company Name - Cognizant
Role - Data Scientist
Q.1 Describe how Gradient Boosting works.
A. Gradient boosting is a type of machine learning boosting. It relies on the intuition that the best possible next model, when combined with previous models, minimizes the overall prediction error. If a small change in the prediction for a case causes no change in error, then next target outcome of the case is zero. Gradient boosting produces a prediction model in the form of an ensemble of weak prediction models, typically decision trees.
Q.2 Describe the decision tree model.
A. Decision Trees are a type of Supervised Machine Learning where the data is continuously split according to a certain parameter. The leaves are the decisions or the final outcomes. A decision tree is a machine learning algorithm that partitions the data into subsets.
Q.3 What is a neural network?
A. Neural networks are a set of algorithms, modeled loosely after the human brain, that are designed to recognize patterns. They interpret sensory data through a kind of machine perception, labeling or clustering raw input. They, also known as Artificial Neural Networks, are the subset of Deep Learning.
Q.4 Explain the Bias-Variance Tradeoff
A. The biasโvariance tradeoff is the property of a model that the variance of the parameter estimated across samples can be reduced by increasing the bias in the estimated parameters.
Q.5 Whatโs the difference between L1 and L2 regularization?
A. The main intuitive difference between the L1 and L2 regularization is that L1 regularization tries to estimate the median of the data while the L2 regularization tries to estimate the mean of the data to avoid overfitting. That value will also be the median of the data distribution mathematically.
-------------------------------------
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Company Name - Cognizant
Role - Data Scientist
Q.1 Describe how Gradient Boosting works.
A. Gradient boosting is a type of machine learning boosting. It relies on the intuition that the best possible next model, when combined with previous models, minimizes the overall prediction error. If a small change in the prediction for a case causes no change in error, then next target outcome of the case is zero. Gradient boosting produces a prediction model in the form of an ensemble of weak prediction models, typically decision trees.
Q.2 Describe the decision tree model.
A. Decision Trees are a type of Supervised Machine Learning where the data is continuously split according to a certain parameter. The leaves are the decisions or the final outcomes. A decision tree is a machine learning algorithm that partitions the data into subsets.
Q.3 What is a neural network?
A. Neural networks are a set of algorithms, modeled loosely after the human brain, that are designed to recognize patterns. They interpret sensory data through a kind of machine perception, labeling or clustering raw input. They, also known as Artificial Neural Networks, are the subset of Deep Learning.
Q.4 Explain the Bias-Variance Tradeoff
A. The biasโvariance tradeoff is the property of a model that the variance of the parameter estimated across samples can be reduced by increasing the bias in the estimated parameters.
Q.5 Whatโs the difference between L1 and L2 regularization?
A. The main intuitive difference between the L1 and L2 regularization is that L1 regularization tries to estimate the median of the data while the L2 regularization tries to estimate the mean of the data to avoid overfitting. That value will also be the median of the data distribution mathematically.
-------------------------------------
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๐๐ปCheck the Course link in our Telegram Description
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4 Ways to search Data Science jobs on LinkedIn
Today's Interview QnAs
Company Name - Hotstar
Role - Data Analyst
---------------------------------------
Q. What is the statistical power of sensitivity?
A. The statistical power of an A/B test refers to the test's sensitivity to certain magnitudes of effect sizes. More precisely, it is the probability of observing a statistically significant result at level alpha (ฮฑ) if a true effect of a certain magnitude (MEI) is in fact present.
Q. What is the difference between covariance and correlation?
A. โCovarianceโ indicates the direction of the linear relationship between variables. โCorrelationโ on the other hand measures both the strength and direction of the linear relationship between two variables.
Q. What is negative indexing? Why is it needed? Can you give an example for the same in python
A. This means that the index value of -1 gives the last element, and -2 gives the second last element of an array. The negative indexing starts from where the array ends.
example: for list L = [0,2,35,3]; L[-1] will print 3 in Python.
Q. What is the condition for using a t-test or a z-test?
A. z-test is used for it when sample size is large, generally n >30. Whereas t-test is used for hypothesis testing when sample size is small, usually n < 30 where n is used to quantify the sample size.
Q. What is the main difference between overfitting and underfitting?
A. Overfitting is a modeling error which occurs when a function is too closely fit to a limited set of data points. Underfitting refers to a model that can neither model the training data nor generalize to new data.
Q. What is the KNN imputation method?
A. The idea in kNN methods is to identify 'k' samples in the dataset that are similar or close in the space. Then we use these 'k' samples to estimate the value of the missing data points. Each sample's missing values are imputed using the mean value of the 'k'-neighbors found in the dataset.
-------------------------------------
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Company Name - Hotstar
Role - Data Analyst
---------------------------------------
Q. What is the statistical power of sensitivity?
A. The statistical power of an A/B test refers to the test's sensitivity to certain magnitudes of effect sizes. More precisely, it is the probability of observing a statistically significant result at level alpha (ฮฑ) if a true effect of a certain magnitude (MEI) is in fact present.
Q. What is the difference between covariance and correlation?
A. โCovarianceโ indicates the direction of the linear relationship between variables. โCorrelationโ on the other hand measures both the strength and direction of the linear relationship between two variables.
Q. What is negative indexing? Why is it needed? Can you give an example for the same in python
A. This means that the index value of -1 gives the last element, and -2 gives the second last element of an array. The negative indexing starts from where the array ends.
example: for list L = [0,2,35,3]; L[-1] will print 3 in Python.
Q. What is the condition for using a t-test or a z-test?
A. z-test is used for it when sample size is large, generally n >30. Whereas t-test is used for hypothesis testing when sample size is small, usually n < 30 where n is used to quantify the sample size.
Q. What is the main difference between overfitting and underfitting?
A. Overfitting is a modeling error which occurs when a function is too closely fit to a limited set of data points. Underfitting refers to a model that can neither model the training data nor generalize to new data.
Q. What is the KNN imputation method?
A. The idea in kNN methods is to identify 'k' samples in the dataset that are similar or close in the space. Then we use these 'k' samples to estimate the value of the missing data points. Each sample's missing values are imputed using the mean value of the 'k'-neighbors found in the dataset.
-------------------------------------
P.S.: If you wanna crack interviews of Data Scientist, Data Analyst, ML Engineer, Business Analyst and Research analyst, join our Assignments based "AI For ALL" Course.
โ Hands on Guided assignments
โ Capstone End-to-end Projects
โ 1-1 Skype Live Doubt Clearance
โ Frequently Asked Interview QnAs PDF collection
โ Course completion Certificate
It is a Self-Paced course with lifetime access.
๐๐ปCheck the Course link in our Telegram Description
Happy Learning With CloudyML ๐
Anonymous Poll
63%
5 โญ
25%
4 โญ
9%
3 โญ
2%
2 โญ
2%
1 โญ
Today's Interview QnAs
Company Name - Tiger Analytics
Role - Data Scientist
----------------------------------------
Q. Deal with unbalanced binary classification?
A. Techniques to Handle unbalanced Data:
1. Use the right evaluation metrics
2. Use K-fold Cross-Validation in the right way
3. Ensemble different resampled datasets
4. Resample with different ratios
5. Design your own models
Q. Activation function?
A. Activation functions are mathematical equations that determine the output of a neural network model. It is a non-linear transformation that we do over the input before sending it to the next layer of neurons or finalizing it as output.
Q. Dimension reduction?
A. Dimensionality Reduction is used to reduce the feature space with consideration by a set of principal features.
Q. Why is mean square error a bad measure of model performance?
A. Mean Squared Error (MSE) gives a relatively high weight to large errors โ therefore, MSE tends to put too much emphasis on large deviations.
Q. Remove multicollinearity?
A. To remove multicollinearities, we can do two things.
1. We can create new features
2. remove them from our data.
Q. long-tailed distribution ?
A. A long tail distribution of numbers is a kind of distribution having many occurrences far from the "head" or central part of the distribution. Most of occurrences in this kind of distributions occurs at early frequencies/values of x-axis.
Q. Outlier? Deal with it?
A. An outlier is an object that deviates significantly from the rest of the objects. They can be caused by measurement or execution error.
Removing outliers is legitimate only for specific reasons. Outliers can be very informative about the subject-area and data collection process. If the outlier does not change the results but does affect assumptions, you may drop the outlier. Or just trim the data set, but replace outliers with the nearest โgoodโ data, as opposed to truncating them completely.
Q. Example where the median is a better measure than the mean ?
A. If your data contains outliers, then you would typically rather use the median because otherwise the value of the mean would be dominated by the outliers rather than the typical values. In conclusion, if you are considering the mean, check your data for outliers, if any then better choose median.
-------------------------------------
P.S.: If you wanna crack interviews of Data Scientist, Data Analyst, ML Engineer, Business Analyst and Research analyst, join our Assignments based "AI For ALL" Course.
โ Hands on Guided assignments
โ Capstone End-to-end Projects
โ 1-1 Skype Live Doubt Clearance
โ Frequently Asked Interview QnAs PDF collection
โ Course completion Certificate
It is a Self-Paced course with lifetime access.
๐๐ปCheck the Course link in our Telegram Description
Happy Learning With CloudyML ๐
Company Name - Tiger Analytics
Role - Data Scientist
----------------------------------------
Q. Deal with unbalanced binary classification?
A. Techniques to Handle unbalanced Data:
1. Use the right evaluation metrics
2. Use K-fold Cross-Validation in the right way
3. Ensemble different resampled datasets
4. Resample with different ratios
5. Design your own models
Q. Activation function?
A. Activation functions are mathematical equations that determine the output of a neural network model. It is a non-linear transformation that we do over the input before sending it to the next layer of neurons or finalizing it as output.
Q. Dimension reduction?
A. Dimensionality Reduction is used to reduce the feature space with consideration by a set of principal features.
Q. Why is mean square error a bad measure of model performance?
A. Mean Squared Error (MSE) gives a relatively high weight to large errors โ therefore, MSE tends to put too much emphasis on large deviations.
Q. Remove multicollinearity?
A. To remove multicollinearities, we can do two things.
1. We can create new features
2. remove them from our data.
Q. long-tailed distribution ?
A. A long tail distribution of numbers is a kind of distribution having many occurrences far from the "head" or central part of the distribution. Most of occurrences in this kind of distributions occurs at early frequencies/values of x-axis.
Q. Outlier? Deal with it?
A. An outlier is an object that deviates significantly from the rest of the objects. They can be caused by measurement or execution error.
Removing outliers is legitimate only for specific reasons. Outliers can be very informative about the subject-area and data collection process. If the outlier does not change the results but does affect assumptions, you may drop the outlier. Or just trim the data set, but replace outliers with the nearest โgoodโ data, as opposed to truncating them completely.
Q. Example where the median is a better measure than the mean ?
A. If your data contains outliers, then you would typically rather use the median because otherwise the value of the mean would be dominated by the outliers rather than the typical values. In conclusion, if you are considering the mean, check your data for outliers, if any then better choose median.
-------------------------------------
P.S.: If you wanna crack interviews of Data Scientist, Data Analyst, ML Engineer, Business Analyst and Research analyst, join our Assignments based "AI For ALL" Course.
โ Hands on Guided assignments
โ Capstone End-to-end Projects
โ 1-1 Skype Live Doubt Clearance
โ Frequently Asked Interview QnAs PDF collection
โ Course completion Certificate
It is a Self-Paced course with lifetime access.
๐๐ปCheck the Course link in our Telegram Description
Happy Learning With CloudyML ๐
๐1
What is the output of the code?
Anonymous Quiz
57%
CloudyMLCloudyMLCloudyMLCloudyMLCloudyMLCloudyML
12%
CloudyMLCloudyMLCloudyMLCloudyMLCloudyML
6%
C l o u d y M L
26%
Error: invalid syntax
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๐๐จ๐ฆ๐ฉ๐๐ง๐ฒ ๐๐๐ฆ๐ - ๐๐๐ฉ๐ ๐๐ฆ๐ข๐ง๐ข
๐๐จ๐ฅ๐ - ๐๐๐ญ๐ ๐๐๐ข๐๐ง๐ญ๐ข๐ฌ๐ญ
--------------------------------------------
๐1. ๐ฉ-๐ฏ๐๐ฅ๐ฎ๐?
๐ns. p-value is a measure of the probability that an observed difference could have occurred just by random chance. The lower the p-value, the greater the statistical significance of the observed difference. P-value can be used as an alternative to or in addition to pre-selected confidence levels for hypothesis testing.
๐2. ๐๐ง๐ญ๐๐ซ๐ฉ๐จ๐ฅ๐๐ญ๐ข๐จ๐ง ๐๐ง๐ ๐๐ฑ๐ญ๐ซ๐๐ฉ๐จ๐ฅ๐๐ญ๐ข๐จ๐ง?
๐ns. Interpolation is the process of calculating the unknown value from known given values whereas extrapolation is the process of calculating unknown values beyond the given data points.
๐3. ๐๐ง๐ข๐๐จ๐ซ๐ฆ๐๐ ๐๐ข๐ฌ๐ญ๐ซ๐ข๐๐ฎ๐ญ๐ข๐จ๐ง & ๐ง๐จ๐ซ๐ฆ๐๐ฅ ๐๐ข๐ฌ๐ญ๐ซ๐ข๐๐ฎ๐ญ๐ข๐จ๐ง?
๐ns. The normal distribution is bell-shaped, which means value near the center of the distribution are more likely to occur as opposed to values on the tails of the distribution. The uniform distribution is rectangular-shaped, which means every value in the distribution is equally likely to occur.
๐4. ๐๐๐๐จ๐ฆ๐ฆ๐๐ง๐๐๐ซ ๐๐ฒ๐ฌ๐ญ๐๐ฆ๐ฌ?
๐ns. The recommender system mainly deals with the likes and dislikes of the users. Its major objective is to recommend an item to a user which has a high chance of liking or is in need of a particular user based on his previous purchases. It is like having a personalized team who can understand our likes and dislikes and help us in making the decisions regarding a particular item without being biased by any means by making use of a large amount of data in the repositories which are generated day by day.
๐5. ๐๐๐๐ ๐๐ฎ๐ง๐๐ญ๐ข๐จ๐ง ๐ข๐ง ๐๐๐
๐ns. The SQL Joins clause is used to combine records from two or more tables in a database.
๐6. ๐๐ช๐ฎ๐๐ซ๐๐ ๐๐ซ๐ซ๐จ๐ซ ๐๐ง๐ ๐๐๐ฌ๐จ๐ฅ๐ฎ๐ญ๐ ๐๐ซ๐ซ๐จ๐ซ?
๐ns. mean squared error (MSE), and mean absolute error (MAE) are used to evaluate the regression problem's accuracy. The squared error is everywhere differentiable, while the absolute error is not (its derivative is undefined at 0). This makes the squared error more amenable to the techniques of mathematical optimization.
-----------------------
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โ Hands on Guided assignments
โ Capstone End-to-end Projects
โ 1-1 Skype Live Doubt Clearance
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๐๐จ๐ฆ๐ฉ๐๐ง๐ฒ ๐๐๐ฆ๐ - ๐๐๐ฉ๐ ๐๐ฆ๐ข๐ง๐ข
๐๐จ๐ฅ๐ - ๐๐๐ญ๐ ๐๐๐ข๐๐ง๐ญ๐ข๐ฌ๐ญ
--------------------------------------------
๐1. ๐ฉ-๐ฏ๐๐ฅ๐ฎ๐?
๐ns. p-value is a measure of the probability that an observed difference could have occurred just by random chance. The lower the p-value, the greater the statistical significance of the observed difference. P-value can be used as an alternative to or in addition to pre-selected confidence levels for hypothesis testing.
๐2. ๐๐ง๐ญ๐๐ซ๐ฉ๐จ๐ฅ๐๐ญ๐ข๐จ๐ง ๐๐ง๐ ๐๐ฑ๐ญ๐ซ๐๐ฉ๐จ๐ฅ๐๐ญ๐ข๐จ๐ง?
๐ns. Interpolation is the process of calculating the unknown value from known given values whereas extrapolation is the process of calculating unknown values beyond the given data points.
๐3. ๐๐ง๐ข๐๐จ๐ซ๐ฆ๐๐ ๐๐ข๐ฌ๐ญ๐ซ๐ข๐๐ฎ๐ญ๐ข๐จ๐ง & ๐ง๐จ๐ซ๐ฆ๐๐ฅ ๐๐ข๐ฌ๐ญ๐ซ๐ข๐๐ฎ๐ญ๐ข๐จ๐ง?
๐ns. The normal distribution is bell-shaped, which means value near the center of the distribution are more likely to occur as opposed to values on the tails of the distribution. The uniform distribution is rectangular-shaped, which means every value in the distribution is equally likely to occur.
๐4. ๐๐๐๐จ๐ฆ๐ฆ๐๐ง๐๐๐ซ ๐๐ฒ๐ฌ๐ญ๐๐ฆ๐ฌ?
๐ns. The recommender system mainly deals with the likes and dislikes of the users. Its major objective is to recommend an item to a user which has a high chance of liking or is in need of a particular user based on his previous purchases. It is like having a personalized team who can understand our likes and dislikes and help us in making the decisions regarding a particular item without being biased by any means by making use of a large amount of data in the repositories which are generated day by day.
๐5. ๐๐๐๐ ๐๐ฎ๐ง๐๐ญ๐ข๐จ๐ง ๐ข๐ง ๐๐๐
๐ns. The SQL Joins clause is used to combine records from two or more tables in a database.
๐6. ๐๐ช๐ฎ๐๐ซ๐๐ ๐๐ซ๐ซ๐จ๐ซ ๐๐ง๐ ๐๐๐ฌ๐จ๐ฅ๐ฎ๐ญ๐ ๐๐ซ๐ซ๐จ๐ซ?
๐ns. mean squared error (MSE), and mean absolute error (MAE) are used to evaluate the regression problem's accuracy. The squared error is everywhere differentiable, while the absolute error is not (its derivative is undefined at 0). This makes the squared error more amenable to the techniques of mathematical optimization.
-----------------------
P.S.: If you wanna crack interviews of Data Scientist, Data Analyst, ML Engineer, Business Analyst and Research analyst, join our Assignments based "AI For ALL" Course.
โ Hands on Guided assignments
โ Capstone End-to-end Projects
โ 1-1 Skype Live Doubt Clearance
โ Frequently Asked Interview QnAs PDF collection
โ Course completion Certificate
It is a Self-Paced course with lifetime access.
๐๐ปCheck the Course link in our Telegram Description
Happy Learning With CloudyML ๐