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
๐๐จ๐๐๐ฒ'๐ฌ ๐๐ง๐ญ๐๐ซ๐ฏ๐ข๐๐ฐ ๐๐ฎ๐๐ฌ๐ญ ๐ ๐๐ง๐ฌ
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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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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Today's Interview QnAs
Company Name - Cognizant
Role - Data Scientist
-------------------------------------
Q. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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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Today's Interview QnAs
Company Name - Genpact
Role - Data Scientist
----------------------------
Q. Inter quartile ranges?
A. The most effective way to find all of your outliers is by using the interquartile range (IQR). The IQR contains the middle bulk of your data, so outliers can be easily found once you know the IQR. Quartiles divide the entire set into four equal parts. So, there are three quartiles, first, second and third represented by Q 1 , Q 2 and Q 3 , respectively. Q 2 is nothing but the median.
Q. Imputation methods?
A. They are:
List wise or case deletion
Pairwise deletion
Mean substitution
Regression imputation
Maximum likelihood.
Q. Deal overfittting?
A. Techniques to reduce overfitting:
Increase training data.
Reduce model complexity.
Early stopping during the training phase.
Ridge Regularization and Lasso Regularization.
Q. Gridsearch vs Random search?
A. Random search differs from grid search in that we no longer provide an explicit set of possible values for each hyperparameter; rather, we provide a statistical distribution for each hyperparameter from which values are sampled. Essentially, we define a sampling distribution for each hyperparameter to carry out a randomized search.
Q. Hyperparameters in SVM?
A. kernel: It maps the observations into some feature space. Ideally the observations are more easily (linearly) separable after this transformation. There are multiple standard kernels for this transformations, e.g. the linear kernel, the polynomial kernel and the radial kernel.
C: It is a hypermeter in SVM to control error. The C parameter tells the SVM optimization how much you want to avoid misclassifying each training example. For large values of C, the optimization will choose a smaller-margin hyperplane if that hyperplane does a better job of getting all the training points classified correctly.
gamma: Gamma is used when we use the Gaussian RBF kernel. if you use linear or polynomial kernel then you do not need gamma only you need C hypermeter. Somewhere it is also used as sigma. Gamma decides that how much curvature we want in a decision boundary.
Q. Ridge vs lasso?
A. Ridge and Lasso regression are some of the simple techniques to reduce model complexity and prevent over-fitting which may result from simple linear regression . Ridge Regression : In ridge regression, the cost function is altered by adding a penalty equivalent to square of the magnitude of the coefficients. lasso (least absolute shrinkage and selection operator; also Lasso or LASSO) is a regression analysis method that performs both variable selection and regularization in order to enhance the prediction accuracy and interpretability of the resulting statistical model.
--------------------------
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Company Name - Genpact
Role - Data Scientist
----------------------------
Q. Inter quartile ranges?
A. The most effective way to find all of your outliers is by using the interquartile range (IQR). The IQR contains the middle bulk of your data, so outliers can be easily found once you know the IQR. Quartiles divide the entire set into four equal parts. So, there are three quartiles, first, second and third represented by Q 1 , Q 2 and Q 3 , respectively. Q 2 is nothing but the median.
Q. Imputation methods?
A. They are:
List wise or case deletion
Pairwise deletion
Mean substitution
Regression imputation
Maximum likelihood.
Q. Deal overfittting?
A. Techniques to reduce overfitting:
Increase training data.
Reduce model complexity.
Early stopping during the training phase.
Ridge Regularization and Lasso Regularization.
Q. Gridsearch vs Random search?
A. Random search differs from grid search in that we no longer provide an explicit set of possible values for each hyperparameter; rather, we provide a statistical distribution for each hyperparameter from which values are sampled. Essentially, we define a sampling distribution for each hyperparameter to carry out a randomized search.
Q. Hyperparameters in SVM?
A. kernel: It maps the observations into some feature space. Ideally the observations are more easily (linearly) separable after this transformation. There are multiple standard kernels for this transformations, e.g. the linear kernel, the polynomial kernel and the radial kernel.
C: It is a hypermeter in SVM to control error. The C parameter tells the SVM optimization how much you want to avoid misclassifying each training example. For large values of C, the optimization will choose a smaller-margin hyperplane if that hyperplane does a better job of getting all the training points classified correctly.
gamma: Gamma is used when we use the Gaussian RBF kernel. if you use linear or polynomial kernel then you do not need gamma only you need C hypermeter. Somewhere it is also used as sigma. Gamma decides that how much curvature we want in a decision boundary.
Q. Ridge vs lasso?
A. Ridge and Lasso regression are some of the simple techniques to reduce model complexity and prevent over-fitting which may result from simple linear regression . Ridge Regression : In ridge regression, the cost function is altered by adding a penalty equivalent to square of the magnitude of the coefficients. lasso (least absolute shrinkage and selection operator; also Lasso or LASSO) is a regression analysis method that performs both variable selection and regularization in order to enhance the prediction accuracy and interpretability of the resulting statistical model.
--------------------------
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Today's Interview Quest
Company Name - Tiger Analytics
Role - Data Scientist
---------------------------------------
Q. Explain the difference between an array and a linked list.
A. An array is a collection of elements of a similar data type. A linked list is a collection of objects known as a node where node consists of two parts, i.e., data and address. Array elements store in a contiguous memory location. Linked list elements can be stored anywhere in the memory or randomly stored.
Q. How do you ensure you are not overfitting a model?
A. Keep your model simple. Use regularization technique. Use cross-validation.
Q. How do you fix high variance in a model?
A. You can reduce High variance, by reducing the number of features in the model. There are several methods available to check which features don't add much value to the model and which are of importance. Increasing the size of the training set can also help the model generalize.
Q. What are hyperparameters? How do they differ from model parameters?
A. Model Parameters: These are the parameters in the model that must be determined using the training data set. These are the fitted parameters. Hyperparameters: These are adjustable parameters that must be tuned in order to obtain a model with optimal performance.
Q. You are told that your regression model is suffering from multicollinearity. How do verify this is true and build a better model?
A. A simple method to detect multicollinearity in a model is by using something called the variance inflation factor or the VIF for each predicting variable. We can follow these steps in order to build a better model:
Remove some of the highly correlated independent variables.
Linearly combine the independent variables, such as adding them together.
Perform an analysis designed for highly correlated variables, such as principal components analysis or partial least squares regression.
Q. You build a random forest model with 10,000 trees. Training error as at 0.00, but validation error is 34.23. Explain what went wrong ?
A. It means that the model has mimicked the training pattern perfectly that it will cause overfitting problem in test samples. To avoid this overfitting, use techniques like less complex model or cross validation etc.
Q. What is the recall, specificity and precision of the confusion matrix?
A. The recall is the ratio of the relevant results returned by the search engine to the total number of the relevant results that could have been returned. Specificity (SP) is calculated as the number of correct negative predictions divided by the total number of negatives. The precision is the proportion of relevant results in the list of all returned search results.
------------------------------------
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Company Name - Tiger Analytics
Role - Data Scientist
---------------------------------------
Q. Explain the difference between an array and a linked list.
A. An array is a collection of elements of a similar data type. A linked list is a collection of objects known as a node where node consists of two parts, i.e., data and address. Array elements store in a contiguous memory location. Linked list elements can be stored anywhere in the memory or randomly stored.
Q. How do you ensure you are not overfitting a model?
A. Keep your model simple. Use regularization technique. Use cross-validation.
Q. How do you fix high variance in a model?
A. You can reduce High variance, by reducing the number of features in the model. There are several methods available to check which features don't add much value to the model and which are of importance. Increasing the size of the training set can also help the model generalize.
Q. What are hyperparameters? How do they differ from model parameters?
A. Model Parameters: These are the parameters in the model that must be determined using the training data set. These are the fitted parameters. Hyperparameters: These are adjustable parameters that must be tuned in order to obtain a model with optimal performance.
Q. You are told that your regression model is suffering from multicollinearity. How do verify this is true and build a better model?
A. A simple method to detect multicollinearity in a model is by using something called the variance inflation factor or the VIF for each predicting variable. We can follow these steps in order to build a better model:
Remove some of the highly correlated independent variables.
Linearly combine the independent variables, such as adding them together.
Perform an analysis designed for highly correlated variables, such as principal components analysis or partial least squares regression.
Q. You build a random forest model with 10,000 trees. Training error as at 0.00, but validation error is 34.23. Explain what went wrong ?
A. It means that the model has mimicked the training pattern perfectly that it will cause overfitting problem in test samples. To avoid this overfitting, use techniques like less complex model or cross validation etc.
Q. What is the recall, specificity and precision of the confusion matrix?
A. The recall is the ratio of the relevant results returned by the search engine to the total number of the relevant results that could have been returned. Specificity (SP) is calculated as the number of correct negative predictions divided by the total number of negatives. The precision is the proportion of relevant results in the list of all returned search results.
------------------------------------
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It is a Self-Paced course with lifetime access.
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Today's Interview Quest
Company Name - Fractal
Role - Data Scientist
---------------------------------------
Q. Difference between array and list
A. The main difference between these two data types is the operation you can perform on them. Lists are containers for elements having differing data types but arrays are used as containers for elements of the same data type.
Q. Which is faster dictionary or list for look up
A. Dictionary is faster because you used a better algorithm. The reason is because a dictionary is a lookup, while a list is an iteration. Dictionary uses a hash lookup, while your list requires walking through the list until it finds the result from beginning to the result each time.
Q. How much time SVM takes to complete if 1 iteration takes 10sec for 1st class And there are 4 classes.
A. It would take 4*10 = 40 seconds to train one-vs-all method one to one.
Q. Kernals in svm, there difference
A. Kernel Function in SVM is a method used to take data as input and transform into the required form of processing data.
Gaussian Kernel Radial Basis Function (RBF) : It is used to perform transformation, when there is no prior knowledge about data and it uses radial basis method to improve the transformation.
Sigmoid Kernel: this function is equivalent to a two-layer, perceptron model of neural network, which is used as activation function for artificial neurons.
Polynomial Kernel: It represents the similarity of vectors in training set of data in a feature space over polynomials of the original variables used in kernel.
Linear Kernel: used when data is linearly separable.
------------------------------
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Company Name - Fractal
Role - Data Scientist
---------------------------------------
Q. Difference between array and list
A. The main difference between these two data types is the operation you can perform on them. Lists are containers for elements having differing data types but arrays are used as containers for elements of the same data type.
Q. Which is faster dictionary or list for look up
A. Dictionary is faster because you used a better algorithm. The reason is because a dictionary is a lookup, while a list is an iteration. Dictionary uses a hash lookup, while your list requires walking through the list until it finds the result from beginning to the result each time.
Q. How much time SVM takes to complete if 1 iteration takes 10sec for 1st class And there are 4 classes.
A. It would take 4*10 = 40 seconds to train one-vs-all method one to one.
Q. Kernals in svm, there difference
A. Kernel Function in SVM is a method used to take data as input and transform into the required form of processing data.
Gaussian Kernel Radial Basis Function (RBF) : It is used to perform transformation, when there is no prior knowledge about data and it uses radial basis method to improve the transformation.
Sigmoid Kernel: this function is equivalent to a two-layer, perceptron model of neural network, which is used as activation function for artificial neurons.
Polynomial Kernel: It represents the similarity of vectors in training set of data in a feature space over polynomials of the original variables used in kernel.
Linear Kernel: used when data is linearly separable.
------------------------------
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Anonymous Quiz
69%
pop()
8%
append()
2%
sort()
21%
remove()
Today's Interview QnAs
Company Name - Larsen and Toubro
Role - Data Scientist
----------------------------------------
Q. Ways to avoid overfitting
A. Some steps that we can take to avoid it:
1. Data augmentation
2. L1/L2 Regularization
3. Remove layers / number of units per layer
4. Cross-validation
Q. Image classification algorithms
A. Image Classification algorithms are the algorithms which are used to classify labels for images using their characteristics. Example: Convolutional Neural Networks.
Q. args will return?
A. The special syntax *args in function is used to pass a variable number of arguments to a function. It is used to pass a non-key worded, variable-length argument list. The syntax is to use the symbol * to take in a variable number of arguments; by convention, it is often used with the word args.
Q. Difference between having and where clause in SQL.
A. WHERE Clause is used to filter the records from the table based on the specified condition. HAVING Clause is used to filter record from the groups based on the specified condition.
Q. How do you handle categorical data?
A. One-Hot Encoding is the most common, correct way to deal with non-ordinal categorical data. It consists of creating an additional feature for each group of the categorical feature and mark each observation belonging (Value=1) or not (Value=0) to that group.
Q. What Is Interpolation And Extrapolation?
A. 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.
Q. SQL joins and Groups
A. The SQL Joins clause is used to combine records from two or more tables in a database. The GROUP BY statement groups rows that have the same values into summary rows, like "find the number of customers in each country".
Q. How do you handle null values and which Imputation method is more favorable?
A. Ways to handle missing values in the dataset:
Deleting Rows with missing values.
Impute missing values for continuous variable.
Impute missing values for categorical variable.
Other Imputation Methods.
Using Algorithms that support missing values.
Prediction of missing values.
Multiple imputation is more advantageous than the single imputation because it uses several complete data sets and provides both the within-imputation and between-imputation variability.
---------------------------------------
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Company Name - Larsen and Toubro
Role - Data Scientist
----------------------------------------
Q. Ways to avoid overfitting
A. Some steps that we can take to avoid it:
1. Data augmentation
2. L1/L2 Regularization
3. Remove layers / number of units per layer
4. Cross-validation
Q. Image classification algorithms
A. Image Classification algorithms are the algorithms which are used to classify labels for images using their characteristics. Example: Convolutional Neural Networks.
Q. args will return?
A. The special syntax *args in function is used to pass a variable number of arguments to a function. It is used to pass a non-key worded, variable-length argument list. The syntax is to use the symbol * to take in a variable number of arguments; by convention, it is often used with the word args.
Q. Difference between having and where clause in SQL.
A. WHERE Clause is used to filter the records from the table based on the specified condition. HAVING Clause is used to filter record from the groups based on the specified condition.
Q. How do you handle categorical data?
A. One-Hot Encoding is the most common, correct way to deal with non-ordinal categorical data. It consists of creating an additional feature for each group of the categorical feature and mark each observation belonging (Value=1) or not (Value=0) to that group.
Q. What Is Interpolation And Extrapolation?
A. 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.
Q. SQL joins and Groups
A. The SQL Joins clause is used to combine records from two or more tables in a database. The GROUP BY statement groups rows that have the same values into summary rows, like "find the number of customers in each country".
Q. How do you handle null values and which Imputation method is more favorable?
A. Ways to handle missing values in the dataset:
Deleting Rows with missing values.
Impute missing values for continuous variable.
Impute missing values for categorical variable.
Other Imputation Methods.
Using Algorithms that support missing values.
Prediction of missing values.
Multiple imputation is more advantageous than the single imputation because it uses several complete data sets and provides both the within-imputation and between-imputation variability.
---------------------------------------
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Today's Interview QnAs
Company Name - Tiger Analytics
Role - Data Scientist
--------------------------------------
Q.Autoencoder methods
A. Autoencoder is a type of neural network where the output layer has the same dimensionality as the input layer. In simpler words, the number of output units in the output layer is equal to the number of input units in the input layer. Various techniques exist to prevent autoencoders from learning the identity function and to improve their ability to capture important ' information and learn richer representations. 1.Sparse autoencoder (SAE) 2. Denoising autoencoder (DAE) 3. Contractive autoencoder (CAE) 4. Principal component analysis.
Q. L1 and L2 regularization?
A. L1 regularization gives output in binary weights from 0 to 1 for the model's features and is adopted for decreasing the number of features in a huge dimensional dataset. L2 regularization disperse the error terms in all the weights that leads to more accurate customized final models.
Q. How to measure the Euclidean distance betweeen the two arrays in numpy?
A. Euclidean distance is defined in mathematics as the magnitude or length of the line segment between two points. There are multiple methods for measuring the euclidean methods.
Method 1. In this method, we first initialize two numpy arrays. Then, we use linalg.norm() of numpy basically to compute the euclidean distance directly.
Method 2. In this method, we first initialize two numpy arrays. Then, we take the difference of the two arrays, compute the dot product of the result, and transpose of the result. Then we take the square root of the answer. This is another way to implement Euclidean distance.
Method 3. In this method, we first initialize two numpy arrays. Then, we compute the difference of these arrays and take their square. We take the sum of the squared elements, and after that, we take the square root in the end. This is another way to implement Euclidean distance.
Q.What are the support vectors in SVM?
A. Support vectors are data points that are closer to the hyperplane and influence the position and orientation of the hyperplane. Using these support vectors, we maximize the margin of the classifier. Deleting the support vectors will change the position of the hyperplane. These are the points that help us build our SVM.
Q. How do you handle categorical data?
A. One-Hot Encoding is the most common, correct way to deal with non-ordinal categorical data. It consists of creating an additional feature for each group of the categorical feature and mark each observation belonging (Value=1) or not (Value=0) to that group.
Q. What is coerrelation?
A.Correlation is a statistical measure that expresses the extent to which two variables are linearly related (meaning they change together at a constant rate). It's a common tool for describing simple relationships without making a statement about cause and effects
Q. What is covariance?
A. Covariance is nothing but a measure of correlation. Covariance is a measure of how much two random variables vary together. Itโs similar to variance, but where variance tells you how a single variable varies, co variance tells you how two variables vary together
------------------
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Company Name - Tiger Analytics
Role - Data Scientist
--------------------------------------
Q.Autoencoder methods
A. Autoencoder is a type of neural network where the output layer has the same dimensionality as the input layer. In simpler words, the number of output units in the output layer is equal to the number of input units in the input layer. Various techniques exist to prevent autoencoders from learning the identity function and to improve their ability to capture important ' information and learn richer representations. 1.Sparse autoencoder (SAE) 2. Denoising autoencoder (DAE) 3. Contractive autoencoder (CAE) 4. Principal component analysis.
Q. L1 and L2 regularization?
A. L1 regularization gives output in binary weights from 0 to 1 for the model's features and is adopted for decreasing the number of features in a huge dimensional dataset. L2 regularization disperse the error terms in all the weights that leads to more accurate customized final models.
Q. How to measure the Euclidean distance betweeen the two arrays in numpy?
A. Euclidean distance is defined in mathematics as the magnitude or length of the line segment between two points. There are multiple methods for measuring the euclidean methods.
Method 1. In this method, we first initialize two numpy arrays. Then, we use linalg.norm() of numpy basically to compute the euclidean distance directly.
Method 2. In this method, we first initialize two numpy arrays. Then, we take the difference of the two arrays, compute the dot product of the result, and transpose of the result. Then we take the square root of the answer. This is another way to implement Euclidean distance.
Method 3. In this method, we first initialize two numpy arrays. Then, we compute the difference of these arrays and take their square. We take the sum of the squared elements, and after that, we take the square root in the end. This is another way to implement Euclidean distance.
Q.What are the support vectors in SVM?
A. Support vectors are data points that are closer to the hyperplane and influence the position and orientation of the hyperplane. Using these support vectors, we maximize the margin of the classifier. Deleting the support vectors will change the position of the hyperplane. These are the points that help us build our SVM.
Q. How do you handle categorical data?
A. One-Hot Encoding is the most common, correct way to deal with non-ordinal categorical data. It consists of creating an additional feature for each group of the categorical feature and mark each observation belonging (Value=1) or not (Value=0) to that group.
Q. What is coerrelation?
A.Correlation is a statistical measure that expresses the extent to which two variables are linearly related (meaning they change together at a constant rate). It's a common tool for describing simple relationships without making a statement about cause and effects
Q. What is covariance?
A. Covariance is nothing but a measure of correlation. Covariance is a measure of how much two random variables vary together. Itโs similar to variance, but where variance tells you how a single variable varies, co variance tells you how two variables vary together
------------------
P.S.: Learn Data Science from Zero and become Interview ready at best affordable price with personal doubt clearance support
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โ Capstone End-to-end Projects
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It is a Self-Paced course, so you can learn according to your time availability
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Today's Interview QnAs
Company Name - Bridgei2i Analytics
Role - Data Scientist
Q. Why does overfitting occur?
A. Overfitting happens when a model learns the detail and noise in the training data to the extent that it negatively impacts the performance of the model on new data. This means that the noise or random fluctuations in the training data is picked up and learned as concepts by the model
Q. What is ensemble learning?
A. Ensemble learning is the process by which multiple models, such as classifiers or experts, are strategically generated and combined to solve a particular computational intelligence problem. Ensemble learning is primarily used to improve the (classification, prediction, function approximation, etc.) performance of a model, or reduce the likelihood of an unfortunate selection of a poor one.
Q. What is F1 score?
A. The F1 score is defined as the harmonic mean of precision and recall. As a short reminder, the harmonic mean is an alternative metric for the more common arithmetic mean. It is often useful when computing an average rate. In the F1 score, we compute the average of precision and recall.
Q. What is pickling and unpickling?
A.โPicklingโ is the process whereby a Python object hierarchy is converted into a byte stream, and โunpicklingโ is the inverse operation, whereby a byte stream (from a binary file or bytes-like object) is converted back into an object hierarchy.
Q. What is lambda function?
A. Python Lambda Functions are anonymous function means that the function is without a name. As we already know that the def keyword is used to define a normal function in Python. Similarly, the lambda keyword is used to define an anonymous function in Python.
Q. What is the trade of between bias and variance ?
A. Bias is the simplifying assumptions made by the model to make the target function easier to approximate. Variance is the amount that the estimate of the target function will change given different training data. Trade-off is tension between the error introduced by the bias and the variance.
...........
P.S.: To know how Akash Raj switched his career from a manual Tester in TCS to Data Scientist at Tredence Analytics, click on the link below ๐๐ป
https://bit.ly/Journey-of-a-Data-Scientist
Happy Learning With CloudyML ๐
Company Name - Bridgei2i Analytics
Role - Data Scientist
Q. Why does overfitting occur?
A. Overfitting happens when a model learns the detail and noise in the training data to the extent that it negatively impacts the performance of the model on new data. This means that the noise or random fluctuations in the training data is picked up and learned as concepts by the model
Q. What is ensemble learning?
A. Ensemble learning is the process by which multiple models, such as classifiers or experts, are strategically generated and combined to solve a particular computational intelligence problem. Ensemble learning is primarily used to improve the (classification, prediction, function approximation, etc.) performance of a model, or reduce the likelihood of an unfortunate selection of a poor one.
Q. What is F1 score?
A. The F1 score is defined as the harmonic mean of precision and recall. As a short reminder, the harmonic mean is an alternative metric for the more common arithmetic mean. It is often useful when computing an average rate. In the F1 score, we compute the average of precision and recall.
Q. What is pickling and unpickling?
A.โPicklingโ is the process whereby a Python object hierarchy is converted into a byte stream, and โunpicklingโ is the inverse operation, whereby a byte stream (from a binary file or bytes-like object) is converted back into an object hierarchy.
Q. What is lambda function?
A. Python Lambda Functions are anonymous function means that the function is without a name. As we already know that the def keyword is used to define a normal function in Python. Similarly, the lambda keyword is used to define an anonymous function in Python.
Q. What is the trade of between bias and variance ?
A. Bias is the simplifying assumptions made by the model to make the target function easier to approximate. Variance is the amount that the estimate of the target function will change given different training data. Trade-off is tension between the error introduced by the bias and the variance.
...........
P.S.: To know how Akash Raj switched his career from a manual Tester in TCS to Data Scientist at Tredence Analytics, click on the link below ๐๐ป
https://bit.ly/Journey-of-a-Data-Scientist
Happy Learning With CloudyML ๐
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Learn everything from scratch.
This course covers Python, SQL, Machine Learning, Statistics, Model deployment, AWS Sagemaker and capstone end-to-end projects
Enroll Now ๐๐ปhttps://bit.ly/Switch-to-Data-at-999
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Today's Interview QnAs
Company Name - PWC
Role - Data Scientist
----------------
Q. What is feature scaling and why is it necessary?
A. Feature scaling in machine learning is one of the most critical steps during the pre-processing of data before creating a machine learning model. Scaling can make a difference between a weak machine learning model and a better one.
The most common techniques of feature scaling are Normalization and Standardization.
Q. Difference between likelihood and probability?
A. Probability basically corresponds to finding the chance of something given a sample distribution of the data, while on the other hand, likelihood refers to finding the best distribution of the data given a particular value of some feature or some situation in the data.
While calculating the probability, feature value can be varied, but the characteristics(mean, standard and deviation) of the data distribution cannot be altered.
The likelihood in very simple terms means to increase the chances of a particular situtation to happen by varrying the characteristics of the dataset distribution.
Q. Difference between Sigmoid and Softmax function?
A. The Sigmoid Activation Function is a mathematical function with a recognizable โSโ shaped curve. It is used for the logistic regression and basic neural network implementation. If we want to have a classifier to solve a problem with more than one right answer, the Sigmoid Function is the right choice. We should apply this function to each element of the raw output independently. The return value of Sigmoid Function is mostly in the range of values between 0 and 1 or -1 and 1.
Whereas the Softmax Activation Function, also know as SoftArgMax or Normalized Exponential Function is a fascinating activation function that takes vectors of real numbers as inputs, and normalizes them into a probability distribution proportional to the exponentials of the input numbers. Before applying, some input data could be negative or greater than 1. Also, they might not sum up to 1. After applying Softmax, each element will be in the range of 0 to 1, and the elements will add up to 1. This way, they can be interpreted as a probability distribution. For more clarification, the larger the input number, the larger the probabilities will be
Q. Which machine learning algorithm is known as the lazy learner ?
A. KNN is a Machine Learning algorithm known as a lazy learner. K-NN is a lazy learner because it doesnโt learn any machine learnt values or variables from the training data but dynamically calculates distance every time it wants to classify, hence memorises the training dataset instead.
-------------
P.S.: Learn Data Science from Zero and become Interview ready at best affordable price with personal doubt clearance support
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
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 - PWC
Role - Data Scientist
----------------
Q. What is feature scaling and why is it necessary?
A. Feature scaling in machine learning is one of the most critical steps during the pre-processing of data before creating a machine learning model. Scaling can make a difference between a weak machine learning model and a better one.
The most common techniques of feature scaling are Normalization and Standardization.
Q. Difference between likelihood and probability?
A. Probability basically corresponds to finding the chance of something given a sample distribution of the data, while on the other hand, likelihood refers to finding the best distribution of the data given a particular value of some feature or some situation in the data.
While calculating the probability, feature value can be varied, but the characteristics(mean, standard and deviation) of the data distribution cannot be altered.
The likelihood in very simple terms means to increase the chances of a particular situtation to happen by varrying the characteristics of the dataset distribution.
Q. Difference between Sigmoid and Softmax function?
A. The Sigmoid Activation Function is a mathematical function with a recognizable โSโ shaped curve. It is used for the logistic regression and basic neural network implementation. If we want to have a classifier to solve a problem with more than one right answer, the Sigmoid Function is the right choice. We should apply this function to each element of the raw output independently. The return value of Sigmoid Function is mostly in the range of values between 0 and 1 or -1 and 1.
Whereas the Softmax Activation Function, also know as SoftArgMax or Normalized Exponential Function is a fascinating activation function that takes vectors of real numbers as inputs, and normalizes them into a probability distribution proportional to the exponentials of the input numbers. Before applying, some input data could be negative or greater than 1. Also, they might not sum up to 1. After applying Softmax, each element will be in the range of 0 to 1, and the elements will add up to 1. This way, they can be interpreted as a probability distribution. For more clarification, the larger the input number, the larger the probabilities will be
Q. Which machine learning algorithm is known as the lazy learner ?
A. KNN is a Machine Learning algorithm known as a lazy learner. K-NN is a lazy learner because it doesnโt learn any machine learnt values or variables from the training data but dynamically calculates distance every time it wants to classify, hence memorises the training dataset instead.
-------------
P.S.: Learn Data Science from Zero and become Interview ready at best affordable price with personal doubt clearance support
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
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