Interview Questions.

Hearing approximately an interview always makes us sense jittery. But all of us recognize quite nicely that the whole technique is really worth suffering for as you could turn out to be getting your dream job. A system mastering interview questions is no exception; in reality, it is nowadays one of the popular posts this is plenty in call for. It wishes an entire lot of instruction and perseverance.

You might also land yourself amid great confusion if you think of making ready for everything. What you need to do is recognition at the high subjects a good way to clarify all of your core ideas. These Machine Learning Interview Questions will help you to crack upcoming interviews.

Top Machine mastering Interview Questions and Answers

Let us dig in and look at the pinnacle Machine Learning interview questions and answers under:

Question: What is Machine Learning? And how is it one of a kind from Artificial Intelligence?

Answer: Machine studying is a manner via which a gadget can carry out from its experiment. A dataset is fed into this system this is able to studying from the dataset. Then in the output, it is aware of how to apprehend things fit inside that information set although their gadget on the software has in no way visible that instance earlier than. ML works on sample reputation; however, AI reveals the concept of intelligence and is about training a machine to react as to how any human mind would do.

Question: Define 3 tiers of constructing a version in Machine Learning.

Answer: The three tiers of building a model in ML are:

Model Building

Choosing the appropriate algorithm for the model and teach it consistent with the requirement.

Model Testing

Check the accuracy of the information by way of checking out with facts sets

Applying a version

Make the specified modifications after checking out and use the very last model for actual-time tasks

Question: Explain parametric fashions with examples? How are they specific from non-parametric models.

Answer: Models with a finite variety of parameters are Parametric fashions. We want to know the parameters of the model to predict new records — for example, linear regression, logistic regression, and linear SVMs.

Models with an unbounded wide variety of parameters are Non-parametric models, taking into account extra flexibility. We need to realize the parameters of the model and the state of the data determined to predict new records — for instance, choice timber, okay-nearest friends, and topic models the use of latent Dirichlet analysis.

Question: Differentiate between Type I and Type II blunders.

Answer:

Type I Error: Reject the appropriate null speculation and is a severe error, additionally referred to as fake high-quality. The opportunity of making this mistake is the level of importance. It is claiming that some thing has took place whilst it hasn’t.

Type II Error: Accept a fake null speculation. The probability of making this error mainly relies upon at the sample size and populace variance. This blunders is more likely to arise if the subject is hard to check due to hard sampling or excessive variability. The chance of rejecting a fake null hypothesis is 1- a.Okay.A Power of the take a look at. It is claiming that nothing has occurred even as some thing has took place.

Question: What are the forms of gadget studying? Differentiate between them.

Answer:

Supervised Learning	Unsupervised Learning	Reinforcement Learning
Definition	Taught by labeled data.	Taught without any guidance using unlabeled data.	It is taught by self-learning by interacting with the surrounding environment.
Types of Problems	Regression and classification	Association and clustering	Reward-based
Type of data	Labeled data	Unlabeled data	No pre-defined data
Training	Involves external supervision.	Doesn’t involve supervision	Doesn’t involve supervision
Approach	Maps labeled input to output.	Discovers output by understanding patterns.	Trail and error method to discover output.
Popular Algorithms	Linear regression, KNN	K-means, C-means	Q-Learning

You may additionally read about Supervised and Unsupervised Learning in detail here.

Question: Explain Generalization, Overfitted, and Underfitted

Answer:

Generalization

A model is built and educated on datasets so that it may make correct predictions at the unseen statistics. If the educated model is capable of making those correct predictions on we are able to say that the version is generalized from the schooling set to test set.

Overfitted

When a model is match too intently to the particularities of the training set and gain a model that works well at the schooling set but isn't always capable of generalize to new data is the case of overfitting. In simple phrases, the model become given to many capabilities whilst education that it have become burdened and gave incorrect analysis output.

Underfitted

When a model is simply too simple and doesn’t cowl all of the factors and variability of the statistics, then the model would possibly perform poorly at the training set. This selecting of the too-simple model is underfitting.

Question: What is inductive system studying?

Answer:

Inductive gadget gaining knowledge of includes the system of learning by using examples, in which a device tries to set off a widespread rule from a hard and fast of found times.

Inductive system gaining knowledge of is an inductive step in which you learn a version from a given facts set.

Question: Name some gear which might be used for going for walks the machine learning set of rules in parallel.

Answer: Some of the tools are:

GPUs

Matlab

Map Reduce

Spark

Graphlab

Giraph

Vowpal

Question: What is the distinction between Causation and Correlation? Explain with example.

Answer: Causation is the relationship among two variables such that occurrences of the opposite purpose certainly one of them.

Correlation is the relationship between  variables that are related to every other but no longer caused by every other.

For instance, Inflation reasons the charge fluctuations in petrol and groceries, so inflation has a causation courting with each of them. Between fuel and groceries, there's a correlation that both of them can increase or decrease because of modifications in inflation, however neither of them reasons or influences the alternative one.

Question: Define Sampling. Why will we need it?

Answer: Sampling is a manner of choosing a subset from a goal populace that might serve as its consultant. We use the facts from the sample to apprehend the sample in the community as a whole. Sampling is necessary because often, we can't collect or system the complete data inside a reasonable time. Sampling can be accomplished with several techniques; some of them are Random Sampling, Stratified Sampling, and Clustering Sampling.

Question: State the difference among type and regression

Answer: Classification is a form of supervised studying technique in which the output label is discrete or specific. Regression, alternatively, is a Supervised gaining knowledge of technique this is used to are expecting or non-stop or actual-valued variables.

For instance, predicting a stock charge is a Regression hassle because the inventory rate is a continuous variable which can take actual-values while predicting whether or not the e-mail is unsolicited mail or now not is a Classification trouble because in this situation, the cost is discrete and has most effective  possible benefits sure, or no.

Question: What is stratified sampling?

Answer: Stratified sampling is a opportunity sampling method in which the entire populace is split into specific subgroups referred to as strata than a possibility pattern is drawn proportionally from every layer. For instance, inside the case of binary type, if the ratio of high quality and terrible categorised records had been nine:1, then in stratified sampling, you'll randomly select subsample from each of the effective and negative categorized datasets such that after sampling the ratio remains 9:1.

Question: Define confidence interval

Answer: It is an c programming language estimate that is probable to consist of an unknown populace parameter, the anticipated range being calculated from the given pattern dataset. It is the range of values for which you are sure that the real cost of the variable would lie.

Question: Define conditional probability.

Answer: Conditional probability is the measure of the likelihood of one event, given that one event has took place. Let us don't forget  events are given A and B, then the conditional possibility of A, given B has already came about, is provided as:

Step - 1

wherein stands for the intersection. So, the conditional probability is the joint chance of each the activities divided by means of the possibility of event B.

Question: Explain what Bayes theorem is and why is it beneficial?

Answer: The theorem is used to describe the opportunity of an occasion based at the earlier expertise of other events related to it. For instance, the probability of someone having a particular disorder might be discovered at the symptoms proven.

Bayes theorem is mathematically formulated as:

Step - 2

where A and B are the activities and P(B) ≠ zero. Most of the type we want to locateB), however we realizeA), so we will use Bayes theorem to discover the missing values.

Question: How are True Positive Rate and Recall are related?

Answer: True Positive Rate is similar to Recall, also referred to as sensitivity. The formulation to calculate them:

Step - three

where TP = proper tremendous and FN = false terrible.

Question: What is a probabilistic graphical version?

Answer: A probabilistic graphical version is a robust framework that represents the conditional dependence some of the random variables in a graph shape. It may be utilized in modelling a huge variety of random variables having complex interactions with every different.

Question: What are the two representations of graphical models? Differentiate between them.

Answer: The two branches of the graphical representation of the distribution are Markov Networks and Bayesian Networks. Both of them differ in the set of independence that they could encode.

Bayesian Networks: When a version structure is a Directed Acyclic Graph(DAG), the version represents a factorization of the joint opportunity of all of the random variables. The Bayesian networks seize conditional independence among random variables and reduce the number of parameters required to estimate the joint opportunity distribution.

Markov Networks: They are used while the underlying structure of the version in an undirected graph. They comply with the Markov manner, i.E. Given the contemporary states, the future states could be unbiased of the past states. Markov Network represents the distribution of the collection of the nodes.

Question: How is the k-Nearest Neighbor (okay-NN) set of rules specific from the ok-Means algorithm?

Answer:

The essential difference between these algorithms is that okay-NN is a Supervised set of rules, whereas k-Means is unsupervised.

Ok-NN is a class set of rules, and k-Means is a clustering set of rules.

Okay-NN tries to categorise an statement on its “okay” surrounding buddies. It is also called a lazy learner because it does without a doubt nothing on the schooling degree. On the other hand, the okay-Means set of rules partitions the training information set into extraordinary clusters such that all the records factors from other clusters. The set of rules tries to keep sufficient separability between the clusters.

Question: How is KNN specific from ok-manner clustering?

Answer:

kNN	k-means clustering
Supervised learning algorithm used for classification.	The unsupervised method used for clustering.
Data is labelled for training.	No labelled data, machine trains itself.
The ‘k’ refers to the number of nearest neighbours of a target label.	k refers to the number of clusters, which is set at the beginning of the algorithm
When the algorithm gives the highest possible accuracy, the algorithm stops.	When no more clusters move from one to another, the algorithm is said to be complete.
We can optimize the algorithm using confusion matrix and cross-validation.	Optimization can be performed using silhouette and elbow methods.

Question: Define F-test. Where might you operate it?

Answer: An F-test is any statistical speculation take a look at where the test statistic follows an F-distribution beneath the null speculation. If you have got two fashions which have been fitted to a dataset, you may use F-check to become aware of the model which first-rate fits the pattern population.

Question: What is a chi-squared check?

Answer: Chi-squared is any statistical speculation test in which the take a look at statistic follows a chi-squared distribution (distribution of the sum of the squared general normal deviates) below the null hypothesis. It measures how nicely the located distribution of information suits the predicted distribution if the variables are unbiased.

Question: What is the p-value? Why is it critical?

Answer: The p-value represents the extent of marginal significance even as performing the minimum statistical check. It gives the smallest degree of significance at which the null hypothesis can be rejected. A small p-fee (typically <= 0.05) means that there is strong evidence against the null hypothesis, and therefore, you can refuse the null hypothesis. A significant p-value (>zero.05) indicates weak evidence towards the null speculation, and thus one can not reject the null hypothesis. The smaller the p-cost, the higher the importance with which the null hypothesis may be rejected.

Question: Explain how a ROC curve works.

Answer: ROC curve or Receiver Operating Characteristic Curve is the graphical illustration of the overall performance of a classification model for all of the class thresholds. The graph indicates  parameters, i.E. True Positive Rate (TPR) and False Positive Rate (FPR) at unique classification thresholds. A ordinary ROC curveis as follows:

in which the vertical axis is TPR, and the horizontal axis is FPR. Lowering the threshold will classify more items as advantageous, thereby increasing both TP and FP. To compute ROC, we use a sorting algorithm known as AUC (Area Under the Curve) which measures the complete 2-D place underneath the curve.

Question: Define precision and recall.

Answer: Precision and bear in mind are measures used to assess the performance of a class algorithm. In a super classifier, precision and consider are same to 1. Precision is the fraction of relevant times amongst the retrieved times, whereas recall is the fraction of retrieved instances amongst the relevant times.

Precision = genuine high quality/(true high-quality + fake fine)

Recall = true advantageous/(real fantastic + false poor)

Question: What is the difference between L1 and L2 regularization?

Answer: Both L1 and L2 regularization are carried out to avoid overfitting. L1 tries to calculate the median, while L2 calculates the mean of the records for the same. L1 is also known as Lasso and L2, Ridge regularization method.

In L1 regularization, capabilities that aren't essential are removed, accordingly deciding on simplest the most applicable features. In L2, the loss feature tries to decrease loss through subtracting it from the common (mean) of the distribution of records.

Question: What is the distinction between ‘schooling Set’ and ‘test Set’ in a Machine Learning Model?

Answer: Whenever we achieve a dataset, we split the information into two sets – schooling and checking out. Usually, 70-80% of records is taken for schooling and rest for trying out. The schooling dataset is used to create or construct the version. The test dataset is used to assess and discover the accuracy of the version.

Question: How Do You Handle Missing or Corrupted Data in a Dataset?

Answer: There are many approaches to try this:

put off or drop the lacking rows or columns.

Replace them with every other cost.

Assign them a new class, if a fashion/sample is seen.

Question: What Are the Applications of Supervised Machine Learning in Modern Businesses?

Answer: There are many sensible applications of supervised studying:

photo category

recommender structures

dynamic pricing

client segmentation

perceive the most precious customers (Customer lifetime fee modeling)

Question: What is Semi-supervised Machine Learning?

Answer: Semi-supervised getting to know is an approach that's a mixture of supervised and unsupervised gaining knowledge of mechanism. It combines a small amount of labelled records in conjunction with the large amount of unlabelled statistics to be fed into the device for education purposes. Speech popularity is a superb instance of semi-supervised gaining knowledge of. This sort of ML approach allows while you don’t have enough information and can use the techniques to increase the dimensions of schooling information.

Question: What Are Unsupervised Machine Learning Techniques?

Answer: Unsupervised getting to know strategies are used whilst we don’t have labelled records, i.E. Handiest the enter is understood, and the output is unknown. Patterns, trends and underlying structure is recognized and modelled the use of the unlabelled training dataset. Unsupervised getting to know techniques are greater accurate and predictable. The most famous set of rules is cluster evaluation used for Exploratory Data Analysis (EDA) to get patterns, groupings and trends.

Question: What is an F1 rating?

Answer: The F1 rating is the degree of the model’s accuracy. It is a weighted common of the precision and do not forget of a model. The end result levels between 0 and 1, with 0 being the worst and 1 being the quality version. F1 rating is widely used inside the area of Information Retrieval and Natural Language Processing.

Step - 4

Question: What is the Bayesian Classifier?

Answer: A Bayesian classifier is a probabilistic model which attempts to reduce the chance of misclassification from the education dataset, it calculates the chances of the values of the functions given the magnificence labels and makes use of this facts inside the test dataset to are expecting the class given the function values by using the Bayes rule.

Question: Explain earlier possibility, likelihood, and marginal likelihood in the context of the Naive Bayes Theorem.

Answer: Prior probability is the proportion of structured(binary) variable of the dataset. It is the closest bet you could make approximately the elegance, with none in addition records. For example, Consider a dataset with a based variable binary, junk mail, or not junk mail. The share of unsolicited mail is 75%, and no longer unsolicited mail is 25%. Hence it is able to be expected the chance of the new electronic mail being unsolicited mail is seventy five%.

The likelihood is the probability of classifying a given statement as accurate inside the presence of some other variable. For example, the probability of the phrase “CASH” being used inside the spam message is a chance.

The marginal likelihood is the opportunity of the word “CASH” being utilized in any message.

Question: What is the confusion matrix? Explain it for a 2-magnificence hassle

Answer: A confusion matrix the desk layout which describes the performance of a version at the test dataset for which valid values are acknowledged. For a binary or 2-magnificence class, that may take  values, 0 or false and 1 or real, a confusion matrix may be drawn as:

Predicted Value 0	Predicted Value 1
Actual Value 0	Real Negative (TN)	False Positive (FP)
Actual Value 1	False Negative (FN)	Real Positive (TP)

Question: How can one pick out a classifier based on the size of the training set?

Answer: If the education set is small, the excessive bias/low variance models, inclusive of Naive Bayes, have a tendency to carry out better due to the fact they may be much less possibly to overfit. If the training set, however, is big, then low bias/ excessive variance models, inclusive of Logistic Regression, tend to perform higher due to the fact they can reflect greater complex relationships.

Question: What does the time period choice boundary imply?

Answer: A choice boundary or a decision floor is a hypersurface which divides the underlying feature area into  subspaces, one for every magnificence. If the decision boundary is a hyperplane, then the classes are linearly separable.

Decision Boundary

In the discern above, the pink line is the selection boundary keeping apart the green circle instances from the blue square ones.

Question: Define entropy?

Answer: Entropy is the degree of uncertainty associated with random variable Y. It is the predicted quantity of bits required to speak the value of the variable.

Step - 5

Where P(y) is the probability of Y having the cost y, concerning Decision Trees, the entropy is used to find the quality feature break up at any node.

Question: What is a Decision Tree?

Answer: A Decision Tree uses a tree-like structure, as a predictive module to explicitly represent the choice and choice making. Each inner node of the decision tree is a characteristic, and each ongoing part from that node represents the cost that the feature can take.

In the case of sure functions, the quantity of outgoing edges is the range of various values in that class. In the case of a numerical function, the quantity of outgoing edges is usually two, one wherein the function cost is less than a real-valued amount and different, that's higher.

In the parent beneath, we've a binary output variable having values sure or no and sure capabilities profession, funded, and pension. The profession is the essential characteristic, and primarily based on its advantages, the Decision Tree characteristic branches out, sooner or later predicting the output.

Decision Tree

Question: What do you understand by statistics advantage?

Answer: Information is used to become aware of the exceptional function to break up the given schooling dataset. It selects the cut up S that most reduces the conditional entropy of output Y for schooling set D. In quick, Information Gain is the change in the Entropy, H from a prior kingdom to a brand new nation when splitting on a characteristic:

Information Gain

We calculate Information Gain for all of the capabilities and features with the best advantage is selected as the maximum essential feature amongst all functions.

Question: What is pruning, and why is it important?

Answer: Pruning is a method that reduces the complexity of the very last classifier by putting off the subtrees from it, whose life does no longer effect the accuracy of the version. In pruning, you grow the whole tree and then iteratively prune lower back some nodes till similarly pruning is harmful. This is carried out by comparing the impact of pruning every node on the tuning information set accuracy and greedily eliminating the one that most improves the tuning dataset accuracy.

A trustworthy way of pruning a Decision Tree is to impose a minimal on the number of schooling examples that reach a leaf. Pruning maintains the trees easy with out affecting the overall accuracy. It allows solve the overfitting difficulty by using lowering the size in addition to the complexity of the tree.

Question: Walk me via ok-Nearest Neighbor Algorithm

Answer: okay-NN is a lazy learner algorithm, which means it does not do whatever at the time of the training. Below are the stairs performed at the time of trying out. For any new test examples, ok-NN

first computes its distances from all the examples within the education dataset.

Then selects the k training samples with the bottom stages

and predicts the output label of the check instance through both selecting the most happening label from the chosen schooling examples(in case of type) or by using calculating of them(in case of Regression)

Question: How does the fee of okay range with bias and variance?

Answer: A tremendous cost of ok way a less difficult version as it'd take the common of a huge wide variety of training examples. So, the variance could lower with the aid of increasing the cost of okay. A simpler version means underfitting and outcomes in excessive bias. On the opposite, a small price of okay means that the take a look at instance depends handiest on a small number of schooling examples, and for this reason, it might result in excessive variances and coffee bias.

Question: How could you vary k if there is a noise in the dataset.

Answer: We must growth k to address any noise. A full-size ok fee would average out or nullify any noise or outlier in the given dataset.

Question: How are you able to speed up the version’s category/prediction time?

Answer: There are  ways via which okay-NN’s computation time is progressed.

Edited Nearest Neighbor: Instead of retaining all of the schooling instances, select a subset of them which can nonetheless offer correct classifications. Use both forward selection or backward elimination to select the subset of the times, that could constantly represent different instances.

K-dimensional Tree: It is a clever records shape used to perform nearest neighbor and range searches. A okay-d tree is just like the selection tree except that every internal node shops one data example and splits at the median fee of the feature having excessive variance.

Question: Define Logistic Regression

Answer: Logistic Regression is a statistical approach for analyzing a dataset in which one or greater unbiased information variables determine the final results that could have handiest a constrained number of values, i.E. The reaction variable is specific. Logistic Regression is the move-to approach for the type problem while the reaction variable is binary.

Question: How to educate a Logistic Regression model?

Answer: We use a logistic characteristic for education a Logistic Regression version. Given the enter information x, weight vector w(coefficients of the unbiased variable x) and the opportunity of the output label y, P(y) the logistic function is calculated as:

Step - 6

If P(y) > 0.Five, we expect the output as 1, otherwise zero. Then based on the prediction blunders within the schooling instances, the entire process is repeated by using updating the weights in every generation. The method is stopped once we reach a good sufficient accuracy or entire all the iterators, and the very last weights are used as the values to are expecting the final results of the check instances.

Question: What is the link feature in Logistic Regression?

Answer: A hyperlink function presents the connection between the expected fee of the response variable and the linear predictors. The Logistic Regression uses Logit as its link characteristic, that is the term wx within the equation.

Question: Identify the maximum vital aptitudes of a machine gaining knowledge of engineer?

Answer: Machine gaining knowledge of permits the laptop to learn itself with out being decidedly programmed. It enables the system to examine from revel in after which improve from its errors. The intelligence machine, which is primarily based on device gaining knowledge of, can study from recorded records and past incidents. In-depth expertise of information, possibility, facts modelling, programming language, as well as CS, Application of ML Libraries and algorithms, and software program layout is needed to emerge as a successful machine getting to know engineer.

Question: Indicate the pinnacle intents of machine studying?

Answer: The pinnacle intents of gadget mastering are stated below,

The gadget gets information from the already hooked up computations to give properly-based choices and outputs.

It locates certain styles within the statistics after which makes certain predictions on it to provide answers on subjects.

Question: Who is known as the inventor of Machine Learning?

Answer: Arthur Samuel is referred to as the inventor of Machine Learning. He worked with IBM and developed a computer application for playing checkers. This application that was made within the early Fifties changed into supported by way of the Alpha-beta pruning method due to the low storage facility to be had in computer systems. Hence the first system learning become advanced wherein the machine itself carried out positions of the portions at the board and offered a scoring feature.

Question: Discuss the advantages of Machine Learning?

Answer: Machine studying is a conventional idea, however it has these days gained momentum because of its quantity of benefits. Some of the amazing merits of Machine getting to know are the subsequent:

Effortlessly Recognizes Trends and Patterns: Machine mastering can without difficulty undergo a large amount of data and become aware of sure patterns and developments that are not able to be recognized to humans.

No Human Involvement Required: Machine getting to know includes giving the capability to the device to learn and enhance predictions and algorithms on its very own.

Constant Betterment: Machine mastering famous the great to improve its accuracy and efficiency while the amount of statistics it handles will increase.

Extensive Applications: Machine studying serves a selection of customers and might offer tons greater custom designed revel in to clients as well as also targeting the right client base.

Question: What are the demerits of machine getting to know?

Answer: Although machine getting to know has many merits to it, but it isn’t perfect. There are few limitations to gadget learning that are as follows:

Data acquisition: Machine getting to know wishes a huge quantity of facts to operate on, and the facts requires to be independent and of good excellent.

Interpretation of effects: Sometimes, there may arise problem-related to absolutely the interpretation of effects related to the algorithm. So the set of rules ought to be chosen very carefully for this motive.

High stage of errors susceptibility: An errors is likely to occur inside the state of affairs of gadget studying interface because of the autonomous, unbiased nature of this generation.

Question: List differences between bias and variance?

Answer: Bias is an errors due to the wrong or oversimplified assumptions inside the getting to know algorithm that is for use. This can motive the version to not worthy the records, which causes issue in having excessive predictive accuracy, and it generalizes the knowledge from the schooling set to the testing set.

Whereas variance is a kind of mistakes that happens because of a excessive stage of problem inside the gaining knowledge of set of rules that is being used. This causes the algorithm to be significantly sensitive to the high diploma of variation within the education facts, that can reason the version to overfit the statistics.

Question: What is understood from the term Deep Learning?

Answer: Deep gaining knowledge of is a subdivision of device learning in synthetic intelligence that is associated with neural networks. It has the network functionality of mastering unsupervised from the information, that's unlabelled or unstructured. It is also called a deep neural community or deep neural learning. It contains algorithms which are stimulated by the human brain, that's found out from large amounts of information. It enables in guiding the computer what's evidently seen inside the human that is to analyze from revel in.

Question: What is the use of the F1 rating?

Answer: F1 is a determinant to decide the accuracy of the model. This version shows results in terms of zero and 1, wherein zero indicates the more serious version, and 1 shows the satisfactory model. This version is generally utilized in components of herbal language processing and information retrieval. F1 is notably used in gadget gaining knowledge of, and it does now not don't forget the genuine negatives. It is generally used in type checks wherein true negatives don’t have any most important position.

Question: Highlight the variations between the Generative model and the Discriminative model?

Answer: The goal of the Generative model is to generate new samples from the identical distribution and new records times, Whereas, the Discriminative model highlights the variations among exclusive styles of records times. It attempts to examine at once from the records after which classifies the facts.

Conclusion

I wish this collection of the most essential gadget studying interview questions assist you to get via your interview. This interview may be intimidating as well as overwhelming, so we bring out to you the special rationalization of the above questions to assist to prepare better and crack the interviews with flying hues.

More ML interview questions? Here is a fantastic direction that will help you prepare comprehensively for upcoming ML interviews: Machine Learning Technical Interview.

For top information interview questions preparation, do not forget this e book: Practical Statistics for Data Scientists: 50 Essential Concepts 1st Edition.

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