AI-Mock-Interviewer/PDF_Extracted_Data huggingface dataset:
Question: what is the central limit theorem and why is it important?
Answer: Suppose that we are interested in estimating the average height among all people. Collecting data for every person in the world is impossible. While we cant obtain a height measurement from everyone in the population, we can still sample some people. The question now becomes, what can we say about the average height of the entire population given a single sample. The Central Limit Theorem addresses this question exactly. Read more here .
Question: what is sampling how many sampling methods do you know?
Answer: Data sampling is a statistical analysis technique used to select, manipulate and analyze a representative subset of data points to identify patterns and trends in the l arger data set being examined. Read the full answer here .
Question: what is the difference between type ivs type ii error?
Question: what is linear regression what do the terms p value coefficient and r squared
Question: value mean what is the significance of each of these components?
Question: what are the assumptions required for linear regression?
Answer: There are four major assumptions: 1. There is a linear relationship between the dependent variables and the regres sors, meaning the model you are creating actually fits the data, 2. The errors or residuals of the data are normally distributed and independent from each other, 3. There is minimal multicollinearity between explanatory variables, and 4. Homoscedasticity. This means the variance around the regression line is the same for all values of the predictor variable .
Question: what is a statistical interaction?
Answer: Basically, an interaction is when the effect of one factor (input variable) on the dependent variable (output varia ble) differs among levels of another factor. Read more here .
Question: what is selection bias?
Question: what is an example of a dataset with a non gaussian distribution?
Answer: The Gaussian distribution is part of the Exponential family of distributions, but there are a lot more of them, with the same sort of ease of use, in many cases, and if the person doing th e machine learning has a solid grounding in statistics, they can be utilized where appropriate. Read more here .
Question: what is the binomial probability formula?
Answer: The binomial distribution consists of the probabilities of each of the possible numbers of successes on N trials for independent events that each have a pro bability of (the Greek letter pi) of occurring. Read more Data Science :
Question: what is data science list the differences between supervised and unsupervised
Answer: learning. Data Science is a blend of various tools, algorithms, and machine learning principles wi th the goal to discover
Question: hidden patterns from the raw data how is this different from what statisticians have been doing for years?
Question: what is bias variance tradeoff?
Question: what is a confusion matrix?
Question: what is the difference between long and wide format data?
Question: what do you understand by the term normal distribution?
Answer: Data is usually distributed in different ways with a bias to the left or to the right or it can all be jumbled up. However, there are chances that data is distributed around a central value without any bias to the left or right and reaches normal distribution in the form of a bell -shaped curve. Figure: Normal distribution in a bell curve The random variables are distributed in the form of a symmetrical, bell -shaped curve. Properties of Normal Distribution are as follows; 1. Unimodal -one mode 2. Symmetrical -left and right halves are mirror images 3. Bell-shaped -maximum height (mode) at the mean 4. Mean, Mode, and Median are all located in the cen ter 5. Asymptotic
Question: what is correlation and covariance in statistics?
Question: what is the difference between point estimates and confidence interval?
Answer: Point Estimation gives us a particular value as an estimate of a populatio n parameter. Method of Moments and Maximum Likelihood estimator methods are used to derive Point Estimators for population parameters. A confidence interval gives us a r ange of values which is likely to contain the population parameter. The confidence inte rval is generally preferred, as it tells us how likely this interval is to contain the population parameter. This likeliness or probability is called Confidence Level or Confidence coefficient and represented by 1 alpha, where alpha is the level of signi ficance.
Question: what is the goal of ab testing?
Question: what is p value?
Question: what is the probability that you see at least one shooting star in the period of an hour?
Answer: Probability of not seeing any shooting star in 15 minutes is = 1 P( Seeing one shooting s tar ) = 1 0.2 = 0.8 Probability of not seeing any shooting star in the period of one hour = (0.8) ^ 4 = 0.4096 Probability of see ing at least one shooting star in the one hour = 1 P( Not seeing any star ) = 1 0.4096 = 0.5904
Question: how can you generate a random number between 17 with only a die?
Question: probability that they have two girls?
Question: is also ahead?
Answer: There are two ways of choosing the coin. One is to pick a fair coin and the other is to pick the one with two heads. Probability of sele cting fair coin = 999/1000 = 0.999 Probability of selecting unfair coin = 1/1000 = 0.001 Selecting 10 heads in a row = Selecting fair coin * Getting 10 heads + Selecting an unfair coin P (A) = 0.999 * (1/ 2)^5 = 0.999 * (1/1024) = 0.000976 P (B) = 0.001 * 1 = 0.001 P( A / A + B ) = 0.000976 / (0.000976 + 0.001) = 0.4939 P( B / A + B ) = 0.001 / 0.001976 = 0.5061 Probability of selecting another head = P(A/A+B) * 0.5 + P(B/A+B) * 1 = 0.4939 * 0.5 + 0.5061 = 0.7531
Question: what do you understand by statistical power of sensitivity and how do you calculate it?
Answer: Sensitivity is commonly used to validate the accuracy of a classifier (Logistic, SVM, Random Forest etc.). Sensitivity is nothing but Pred icted True events/ Total events. True events he re are the events which were true and model also predicted them as true. Calculation of seasonality is pretty straightforward. Seasonality = ( True Positives ) / ( Positives in Actual Dependent Variable )
Question: why is resampling done?
Answer: Resampling is done in any of these cases: Estimating the accuracy of sample statistics by using subsets of accessible data or drawing randomly with replacement from a set of data points Substituting labels on data points when per forming significance tests Validating models by using random subsets (bootstrapping, cross -validation)
Question: what are the differences between overfitting and under fitting?
Question: how to combat overfitting and under fitting?
Answer: To combat overfi tting and underfitting, you can resample the data to estimate the model accuracy (k -fold cross -validation) and by having a validation dataset to evaluate the model.
Question: what is regularisation why is it useful?
Answer: Data Scientist Masters Program Explore Curriculum Regularisation is the process of adding tuning parameter to a model to induce smoothness in order to prevent overfitting. This is mo st often done by adding a constant multiple to an existing weight vector. This constant is often the L1(Lasso) or L2(ridge). The model pre dictions should then minimize the loss function calculated on the regularized training set.
Question: what is the law of large numbers?
Question: what are confounding variables?
Question: what are the types of biases that can occur during sampling?
Answer: Selection bias Under coverage bias Survivorship bias
Question: what is survivorship bias?
Question: what is selection bias?
Answer: Selection bias occurs when the sample obtained is not representative of the population intended to be analysed.
Question: explain how a roc curve works?
Question: what is tf idf vectorization?
Question: why we generally use soft max nonlinearity function as last operation in network?
Question: python orr which one would you prefer for text analytics?
Answer: We will prefer Python because of the following reasons: Python would be the best option because it has Pandas library that provides easy to use data structures and high -performance data analysis tools. R is more suitable for machine le arning than j ust text analysis. Python performs faster for all types of text analytics.
Question: how does data cleaning plays a vital role in the analysis?
Question: what is cluster sampling?
Question: what is systematic sampling?
Question: what are eigenvectors and eigenvalues?
Question: can you cite some examples where a false positive is important than a false negative?
Question: actually purchased anything but are marked as having made 10000 worth of purchase follow steven our if or more ai and data science posts httpslnkdingzu463xq36 can you cite some examples where a false negative important than a false positive?
Answer: Examp le 1: Assume there is an airport A which has received high -security threats and based on certain characteristics they identify whether a particular passenger can be a threat or not. Due to a shortage of staff, they decide to scan passe ngers being predict ed as risk positives by their predictive model. What will happen
Question: if a true threat customer is being flagged as non threat by airport model?
Question: example 2whatifjuryor judge decides to make a criminal go free?
Answer: Example 3 : What if you rejec ted to marry a very good person based on your predictive model and you
Question: happen to meet him her after a few years and realize that you had a false negative?
Answer: Q37. Can you cite some examples where both false positive and false negatives are equally
Question: important?
Question: can you explain the difference between a validation set and a test set?
Question: what is machine learning?
Question: what is supervised learning?
Question: what is unsupervised learning?
Question: what are the various classification algorithms?
Answer: The diagram lists the most important classification algorithms .
Question: what is naive in a naive bayes?
Question: what are the support vectors in svm?
Question: what are the different kernels in svm?
Question: what are entropy and information gain in decision tree algorithm?
Question: what is pruning in decision tree?
Answer: Pruning is a technique in machine learning and search algorithms that reduces the size of decision trees by removing sections of the tree that provide little power to classify instances. S o, when we remove sub-node s of a decision node, this process is called pruning or opposite process of splitting.
Question: what is logistic regression state an example when you have used logistic
Question: what is linear regression?
Question: what are the drawbacks of the linear model?
Question: what is the difference between regression and classification ml techniques?
Question: what are recommender systems?
Question: what is collaborative filtering?
Question: how can outlier values be treated?
Question: what are the various steps involved in an analytics project?
Answer: The following are the various steps involved in an analytics project : 1. Understand the Business problem 2. Explore the data and become familiar with it. 3. Prepare the data for modelling by detecting outliers, trea ting missing values, transforming variables, etc. 4. After data preparation, start running the model, analyze the result and tweak the approach. This is an iterative step until the best possible outcome is achieved. 5. Validate the model using a ne w data set. 6. Start implementing the model and track the result to analyze the performance of the model over the period of time.
Question: during analysis how do you treat missing values?
Question: follow steven our if or more ai and data science posts httpslnkdingzu463xq60how will you define the number of clusters in a clustering algorithm?
Question: what is ensemble learning?
Question: describe in brief any type of ensemble learning?
Question: what is a random forest how does it work?
Question: how do you work towards a random forest?
Answer: The underlying principle of this technique is that several weak learners combined to provide a keen learner. The steps involved are Build several decision trees on bootstrapped training samples of data On each tree, each time a split is considered, a random sample of mm predictors is chosen as split candidates, out of all pp predictors Rule of thumb: At eac h split m=pm=p Predictions: At the majority rule
Question: what cross validation technique would you use on a time series data set?
Question: what is a box cox transformation?
Question: how regularly must an algorithm be updated?
Question: gb dataset how would you go about this problem have you ever faced this kind of
Question: problem in your machine learning data science experience so far?
Question: what do you mean by deep learning?
Answer: Deep Learning is nothing but a paradigm of mach ine learning which has shown incred ible promise in recent years. This is because of the fact that Deep Learning shows a great analogy with the functioning of the human brain.
Question: what is the difference between machine learning and deep learning?
Answer: Machine l earning is a field of computer science that gives computers the ability to learn without being explicitly programmed. Machine learning can be categorised in the following three categories. 1. Supervised machine learning, 2. Unsupervised machine learning, 3. Reinfor cement learning Deep Learning is a subfield of machine learning concerned with algorithms inspired by the structure and function of the brain called artificial neural networks. Q71. What, in your opinion, is the reason for the popularity of Deep Learning in recent
Question: times?
Question: what is reinforcement learning?
Answer: Reinforcement Learning is learning what to do and how to map situations to actions. The end result is to maximise the numerical reward signal. The learner is not told which acti on to take but in stead must discover which action will yield the maximum reward. Reinforcement learning is inspired by the learning of human beings, it is based on the reward/penalty mechanism.
Question: what are artificial neural networks?
Answer: Artificial Neural ne tworks are a spec ific set of algorithms that have revolutionized machine learning. They are inspired by biological neural networks. Neural Networks can adapt to changing the i nput so the netwo rk generates the best possible result without needing to redesign the output criteria.
Question: describe the structure of artificial neural networks?
Question: how are weights initialized in a network?
Question: what is the cost function?
Question: what are hyperparameters?
Question: what will happen if the learning rate is set in accurately too low or too high?
Question: what is the difference between epoch batch and iteration in deep learning?
Question: what are the different layers on cnn?
Question: pooling on cnn and how does it work?
Question: what are recurrent neural networks r nns?
Question: how does an lst m network work?
Question: what is a multilayer perceptron mlp?
Question: exploding gradients?
Question: what is vanishing gradients?
Question: what are the variants of backpropagation?
Question: what are the different deep learning frameworks?
Question: what is the role of the activation function?
Answer: The Activation function is used to introduce non-linearity into the neural netwo rk helping it to learn more complex function. Without which the neural network would be only able to learn linear function which is a linear combination of its input data. An activation function is a function in an artifi cial neuron that delivers an output based on inputs. Q93. Name a few Machine Learning libraries for various purposes . Purpose Libraries Scientific Computation Numpy Tabular Data Pandas Data Modelling & Pr eprocessing Scikit Learn Time -Series Analysis Statsmodels Text processing Regular Expressions, NLTK Deep Learning Tensorflow, Pytorch
Question: what is an auto encoder?
Answer: Auto -enco ders are simple learning networks that aim to transform inputs into outputs with the minimum possible error. This means that we want the output to be as close to input as possible. We add a couple of layers between the input and the output, and the sizes of these layers are smaller than the input layer. The auto-encoder receives unlabelled input which is then encoded to reconstruct the input.
Question: what is a boltzmann machine?
Answer: Boltzmann machines have a simple learning algorithm that allows them to discove r interesting features that represent complex regularities in the training data. The Boltzmann machine is basically used to optimise the weights and the quantity for the g iven problem. The learning algorithm is very slow in networks with many layers of fea ture detectors. Restricted Boltzmann Machines algorithm has a single layer of feature detectors which makes it faster than the rest.
Question: follow steven our if or more ai and data science posts httpslnkdingzu463xq96 what is dropout and batch normalization?
Question: descent?
Question: why is tensor flow the most preferred library in deep learning?
Question: what do you mean by tensor in tensor flow?
Question: what is the computational graph?
Question: what are the differences between supervised and unsupervised learning?
Answer: Supervised Learning Unsupervise d Learning Uses known and labeled data as input Supervised learning has a feedback mechanism Most commonly used supervised learning algorithms are decision trees, logistic regression, and support vector machine Uses unlabeled data as input Unsupervised l earning has no feedback mechanism Most commonly used unsupervised learning algorithms are k -means clustering, hierarchical clustering, and apriori algorithm
Question: how is logistic regression done?
Question: how do you build a random forest model?
Question: how can you avoid the overfitting your model?
Question: what are the features election methods used to select the right variables?
Question: value show will you deal with them?
Question: follow steven our if or more ai and data science posts httpslnkdingzu463x110 for the given point show will you calculate the euclidean distance in python?
Answer: plot1 = [1,3] plot2 = [2,5] The Euclidean distance can b e calculated as follows: euclidean_distance = sqrt( (plot1[0] -plot2[0])**2 + (plot1[1] -plot2[1])**2 )
Question: what are dimensionality reduction and its benefits?
Question: how will you calculate eigenvalues and eigenvectors of the following 3x3 matrix?
Question: how should you maintain a deployed model?
Question: how do you find rmse and mse in a linear regression model?
Answer: RMSE a nd MSE are two of the most common measures of accuracy for a linear regression model. RMSE indicates the Root Mea n Square Error. MSE indicates the Mean Square Error.
Question: how can you select k fork means?
Question: what is the significance of pvalue?
Answer: p-value typically 0.05 This indicates strong evidence against the null hypothesis; so you reject the null hypothesis. p-value typically > 0.05 This indicates weak evidence against the null hypothesis, so you accept the null hypothesis. p-value at cutoff 0.05 This is considered to be marginal, meaning it could go either way.
Question: how can a time series data be declared as stationery?
Question: how can you calculate accuracy using a confusion matrix?
Question: result of which algorithm?
Question: what is a generative adversarial network?
Question: performance what can you do about it?
Question: missing values of both categorical and continuous variables?
Answer: K-means clustering Linear regression K-NN (k -nearest neighbor) Decision trees The K nearest neighbor algorithm can be used because it can compute the nearest neighbor and if it doesn't have a value, it just computes the nearest neighbor based on all t he other features. When you're dealing with K -means clustering or linear regressio n, you need to do that in your pre - processing, otherwise, they'll crash. Decision trees also have the same problem, although there is some variance. 126. Below are the eight actual values of the target variable in the train file. What is the
Question: entropy of the target variable?
Answer: [0, 0, 0, 1, 1, 1, 1, 1] Choose the correct answer. 1. -(5/8 log(5/8) + 3/8 log(3/8)) 2. 5/8 log(5/8) + 3/8 log(3/8) 3. 3/8 log(5/8) + 5/8 log(3/8) 4. 5/8 log(3/8) 3/8 log(5/8) The target variable, in this case, is 1. The formula for calculating the entropy is: Putting p=5 and n=8, we get Entropy = A = -(5/8 log(5/8) + 3/8 log(3/8)) 127. We want to predict t he probability of death from heart disease based on three ri sk factors: age, gender, and blood cholesterol level. What is the most appropriate
Question: algorithm for this case?
Question: study?
Answer: Choose the correct option: 1. K-means clustering 2. Linear regression 3. Association rules 4. Decision trees As we are looking for grouping people to gether specifically by four different similarities, it indicates the value of k. T herefore, K -means clustering (answer A) is the most appropriate algorithm for this study. 129. You have run the association rules algorithm on your dataset, and the two rules {banana, apple} => {grape} and {apple, orange} => {grape} have been found to be
Question: relevant what else must be true?
Question: their purchase decisions which analysis method should you use?
Question: what are the feature vectors?
Question: what are the steps in making a decision tree?
Answer: 1. Take the entire data set as input. 2. Look for a split that maximizes the separation of the classes. A split is any test that div ides the data into two sets. 3. Apply the split to the input data ( divide step). 4. Re-apply steps one and two to the divided data. 5. Stop when you meet any stopping criteria. 6. This step is called pruning. Clean up the tree if you went too far doing splits.
Question: what is root cause analysis?
Question: what is logistic regression?
Question: what is collaborative filtering?
Answer: Most recommender systems use this filtering process to find patterns and information by collaborating perspectives, numerous data sources, and several agents.
Question: do gradient descent methods always converge to similar points?
Answer: They do not, because in some cases, they reach a local minima or a local optima point. You would not reach the global optima point. This is governed by the data and the starting conditions.
Question: what is the goal of ab testing?
Answer: This is statistical hypothesis testing for randomized experiments with two variables, A and B. The objective of A/B testing is to detect any changes to a web pag e to maximize or increase the outcome of a strategy.
Question: what are the confounding variables?
Answer: These are extraneous variables in a statistical model that correlates directly or inversely with both the dependent and the independent variable. The estimate fails to account for the confounding factor.
Question: what is star schema?
Question: how regularly must an algorithm be updated?
Answer: You will want to update an algorithm when: You want the model to evolve as dat a streams through infrastructure The underlying data source is changing There is a case of non -stationarity
Question: what are eigenvalue and eigenvector?
Question: why is resampling done?
Answer: Resampling is done in any of these cases: Estimating the accuracy of sample statistics by using subsets of accessible data, or drawing randomly with replacement from a set of data points Substituting labels on data points when perfo rming significance tests Validating models by using random subsets (bootstrapping, cross -validation)
Question: what is selection bias?
Answer: Selection bias, in general, is a problematic situation in which error is introduced due to a non -random population sample.
Question: what are the types of biases that can occur during sampling?
Answer: 1. Selection bias 2. Undercoverage bias 3. Survivorship bias
Question: what is survivorship bias?
Answer: overlooking those that did not because of their lack of prominence. This can lead to wrong conclusions in numerous ways.