Today's Interview Quest N Ans

Company Name - CRED

Role - Data Scientist
__________________

Q1. What do you understand by the term Normal Distribution?

Ans. Normal distribution, also known as the Gaussian distribution, is a probability distribution that is symmetric about the mean, showing that data near the mean are more frequent in occurrence than data far from the mean. In graph form, normal distribution will appear as a bell curve.


Q2. Explain bias, variance trade off.

Ans. ias 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.


Q3. How can you compute significance using p-value

Ans. The p-value is the probability that the null hypothesis is true. (1 – the p-value) is the probability that the alternative hypothesis is true. The smaller the p-value, the stronger the evidence that you should reject the null hypothesis.


Q4. Differentiate between a multi-label classification problem and a multi-class classification problem.

Ans. Multinomial classification is the problem of classifying instances into one of three or more classes (classifying instances into one of two classes is called binary classification). Multi-label classification involves predicting zero or more class labels.


Q5. If the training loss of your model is high and almost equal to the validation loss, what does it mean? What should you do?

Ans. If the training loss is high and validation loss are almost equal, it means that you're avoiding case of overfitting which is good. But, if your loss is high it means your model is going through underfitting. In this case, you can surely increase layers to increase your accuracy and decrease your training loss.


Q6. Why L1 regularizations cause parameter sparsity whereas L2 regularization does not?

Ans. This is due to the shape of Bias region formed by L1 Norm. When compared to L2 Norm, L1 doesn't concede any area around the axes.


Q7. What is the advantage of performing dimensionality reduction before fitting an SVM?

Ans. Support Vector Machine Learning Algorithm performs better in the reduced space. It is beneficial to perform dimensionality reduction before fitting an SVM if the number of features is large when compared to the number of observations.
__________________

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.

👉🏻Check the Course link in our Telegram Description

Happy Learning With CloudyML 💙

Hello Folks,

The Wait is Over !

Today, Get an interactive session from Akash Raj who was a manual tester before becoming a Data Scientist.

He is going to share how he managed to get 8 offers while attending 40+ interviews. How he approached companies in his notice period and grew on LinkedIn.

Topics gonna cover in this webinar:

1. How to grow on LinkedIn ?
2. How to optimize Naukri profile ?
3. How to build Resume ?
4. How to get jobs in Start-up ?
5. How to ask for referrals ?
6. How to negotiate salary with HR ?

NOTE: This is gonna be the first and last FREE Career Hack Webinar by Akash Raj

So Don't Miss This Wonderful Opportunity

Join us today on Zoom meeting, please follow the link below 👇

https://go.cloudyml.com/courses

See you guys at 07:00PM

-CloudyML Team💙

Guys, on high demand we have extended the offer till 11:59PM, today.

Check out the courses and grab this discount offer

👇🏻👇🏻👇🏻

AI For ALL Data Science course

https://www.cloudyml.com/ai-for-all-discount/

----------------------------------

Deep Learning Course

https://www.cloudyml.com/deep-learning/

Those we had joined our webinar today, thank you so much 💙

Today's Quest N Ans

Company Name - TCS

Role - Data Scientist

——————————————

Q1. How can you assess a good logistic model?

Ans. Measuring the performance of Logistic Regression:

1. One can evaluate it by looking at the confusion matrix and count the misclassifications (when using some probability value as the cutoff)

2. One can evaluate it by looking at statistical tests such as the Deviance or individual Z-scores.


Q2. How Regularly Must an Algorithm be Updated?

Ans. It can vary time to time depending upon number of updates happened in the algorithm as per the requirement.



Q3. Why Is Resampling Done?

Ans. Resampling methods are used to ensure that the model is good enough and can handle variations in data. The model does that by training it on the variety of patterns found in the dataset.



Q4. Write the formula to calculate R-square?

Ans. R^2 = 1 - (RSS/TSS) where RSS = sum of squares of residual and TSS = Total sum of squares


Q5. Goal of A/B Testing?

Ans. A/B testing is a basic randomized control experiment. It is a way to compare the two versions of a variable to find out which performs better in a controlled environment.


Q6. Do Gradient Descent Methods at All-Time Converge to a Similar Point?

Ans. No, they always don't. That's because in some cases it reaches a local minima or a local optima point.


Q7. Feature Vectors?

Ans. A feature vector is a vector containing multiple elements about an object. Putting feature vectors for objects together can make up a feature space.


Q8. If p value is more than 0.05 what does it mean?

Ans. A p-value higher than 0.05 (> 0.05) is not statistically significant and indicates strong evidence for the null hypothesis. This means we retain the null hypothesis and reject the alternative hypothesis.

————————————————

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.

👉🏻Check the Course link in our Telegram Description

Happy Learning With CloudyML 💙

Different Between Data Scientist, Data Analyst and Business Analyst

Get the best Data Science learning experience from zero and become Interview ready

Enroll Now👉🏻 https://go.cloudyml.com/Learn-Data-Science-at-999

Offer Ends Today by 11:59PM !

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).

--------------------------------------

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.

👉🏻Check the Course link in our Telegram Description

Happy Learning With CloudyML 💙

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 μ and standard deviation σ 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 it’s significance

Ans. The p-value is the probability that the null hypothesis is true. (1 – 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.

--------------------------------------

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.

👉🏻Check the Course link in our Telegram Description

Happy Learning With CloudyML 💙

Please Introduce Yourself ???

#internship

CloudyML is hiring for NLP Intern.

It will be a Paid Internship

Apply Now👉🏻 https://bit.ly/Apply4internship

Today's Interview QnAs

Company Name - Philips

Role - Data Scientist

................................


Q1. Time Series (ARIMA)?

Ans. ARIMA, short for ‘AutoRegressive Integrated Moving Average’, 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.

👉🏻 Discounted course link in description

Happy Learning With CloudyML 💙

Learn from Scratch and become a Job ready Data Scientist at just Rs 999/- Only.

Enroll Now👉http://bit.ly/Learn-Data-Science-from-zero

Note: This offer is extended for Last one day on learner's demand. Take this before 11:59PM to get the offer.

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 bias–variance 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 What’s 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.

-------------------------------------

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.

👉🏻Check the Course link in our Telegram Description

Happy Learning With CloudyML 💙

4 Ways to search Data Science jobs on LinkedIn

#quiz

Identify the programing language below
------------------------

#include

int main()
{
std::cout << "I love Data Science
";
return 0;
}