Saturday, February 24, 2024

Machine Learning Technical Interview Questions

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What Do You Mean By Genetic Programming

Machine Learning Interview Questions and Answers | Machine Learning Interview Preparation | Edureka

Genetic Programming is a subfield of machine learning that is essentially identical to Evolutionary Algorithms. To perform a user-defined job, genetic programming software systems use an algorithm that uses random mutation, a fitness function, crossover, and numerous generations of evolution. The genetic programming paradigm is based on a process of testing and selecting the best choice from a range of options.

Whats The Difference Between Probability And Likelihood

Probability is the measure of the likelihood that an event will occur that is, what is the certainty that a specific event will occur? Where-as a likelihood function is a function of parameters within the parameter space that describes the probability of obtaining the observed data.So the fundamental difference is, Probability attaches to possible results likelihood attaches to hypotheses.

‘people Who Bought This Also Bought’ Recommendations Seen On Amazon Are A Result Of Which Algorithm

The recommendation engine is accomplished with collaborative filtering. Collaborative filtering explains the behavior of other users and their purchase history in terms of ratings, selection, etc.

The engine makes predictions on what might interest a person based on the preferences of other users. In this algorithm, item features are unknown.

For example, a sales page shows that a certain number of people buy a new phone and also buy tempered glass at the same time. Next time, when a person buys a phone, he or she may see a recommendation to buy tempered glass as well.

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Q5 How Would You Predict Who Will Renew Their Subscription Next Month What Data Would You Need To Solve This What Analysis Would You Do Would You Build Predictive Models If So Which Algorithms

  • Lets assume that were trying to predict renewal rate for Netflix subscription. So our problem statement is to predict which users will renew their subscription plan for the next month.
  • Next, we must understand the data that is needed to solve this problem. In this case, we need to check the number of hours the channel is active for each household, the number of adults in the household, number of kids, which channels are streamed the most, how much time is spent on each channel, how much has the watch rate varied from last month, etc. Such data is needed to predict whether or not a person will continue the subscription for the upcoming month.
  • After collecting this data, it is important that you find patterns and correlations. For example, we know that if a household has kids, then they are more likely to subscribe. Similarly, by studying the watch rate of the previous month, you can predict whether a person is still interested in a subscription. Such trends must be studied.
  • The next step is analysis. For this kind of problem statement, you must use a classification algorithm that classifies customers into 2 groups:
  • Customers who are likely to subscribe next month
  • Customers who are not likely to subscribe next month
  • Would you build predictive models? Yes, in order to achieve this you must build a predictive model that classifies the customers into 2 classes like mentioned above.
  • Machine Learning Interview Questions: Programming


    These machine learning interview questions test your knowledge of programming principles you need to implement machine learning principles in practice. Machine learning interview questions tend to be technical questions that test your logic and programming skills: this section focuses more on the latter.

    Q26: How do you handle missing or corrupted data in a dataset?

    Answer: You could find missing/corrupted data in a dataset and either drop those rows or columns, or decide to replace them with another value.

    In Pandas, there are two very useful methods: isnull and dropna that will help you find columns of data with missing or corrupted data and drop those values. If you want to fill the invalid values with a placeholder value , you could use the fillna method.

    More reading: Handling missing data

    Q27: Do you have experience with Spark or big data tools for machine learning?

    Answer: Youll want to get familiar with the meaning of big data for different companies and the different tools theyll want. Spark is the big data tool most in demand now, able to handle immense datasets with speed. Be honest if you dont have experience with the tools demanded, but also take a look at job descriptions and see what tools pop up: youll want to invest in familiarizing yourself with them.

    More reading: 50 Top Open Source Tools for Big Data

    Q28: Pick an algorithm. Write the pseudo-code for a parallel implementation.

    More reading: Writing pseudocode for parallel programming

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    Define Precision Recall And F1 Score

    The metric used to access the performance of the classification model is Confusion Metric. Confusion Metric can be further interpreted with the following terms:-

    True Positives These are the correctly predicted positive values. It implies that the value of the actual class is yes and the value of the predicted class is also yes.

    True Negatives These are the correctly predicted negative values. It implies that the value of the actual class is no and the value of the predicted class is also no.

    False positives and false negatives, these values occur when your actual class contradicts with the predicted class.

    Now,Recall, also known as Sensitivity is the ratio of true positive rate , to all observations in actual class yesRecall = TP/

    Precision is the ratio of positive predictive value, which measures the amount of accurate positives model predicted viz a viz number of positives it claims.Precision = TP/

    Accuracy is the most intuitive performance measure and it is simply a ratio of correctly predicted observation to the total observations.Accuracy = /

    What Are The Applications Of Supervised Machine Learning In Modern Businesses

    Applications of supervised machine learning include:

    • Email Spam Detection

      Here we train the model using historical data that consists of emails categorized as spam or not spam. This labeled information is fed as input to the model.

    • Healthcare Diagnosis

      By providing images regarding a disease, a model can be trained to detect if a person is suffering from the disease or not.

    • Sentiment Analysis

      This refers to the process of using algorithms to mine documents and determine whether theyre positive, neutral, or negative in sentiment.

    • Fraud Detection

      By training the model to identify suspicious patterns, we can detect instances of possible fraud.

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    What Is The Meaning Of Overfitting In Machine Learning

    Overfitting can be seen in machine learning when a statistical model describes random error or noise instead of the underlying relationship. Overfitting is usually observed when a model is excessively complex. It happens because of having too many parameters concerning the number of training data types. The model displays poor performance, which has been overfitted.

    What Is Entropy In A Decision Tree Algorithm

    BE PREPARED Machine Learning Engineer interview questions

    Entropy is the measure of randomness or disorder in the group of observations. It also determines how a decision tree switches to split data. Entropy is also used to check the homogeneity of the given data. If the entropy is zero, then the sample of data is entirely homogeneous, and if the entropy is one, then it indicates that the sample is equally divided.

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    Stay Sharp With Our Data Science Interview Questions

    For data scientists, the work isn’t easy, but it’s rewarding and there are plenty of available positions out there. These data science interview questions can help you get one step closer to your dream job. So, prepare yourself for the rigors of interviewing and stay sharp with the nuts and bolts of data science.

    Simplilearn’s comprehensive Post Graduate Program in Data Science, in partnership with Purdue University and in collaboration with IBM will prepare you for one of the world’s most exciting technology frontiers.

    Machine Learning Algorithms Interview Questions

    Machine learning algorithms questions assess your conceptual knowledge of machine learning. Companies ask these questions mostly to machine learning and deep learning specialists that would be focusing on the specific building and training of a machine learning model.

    Many times algorithms questions can be asked in different forms. But three of the most common ways are:

    • Comparing differences algorithms
    • Definitions of algorithm terms

    Why do they get asked?

    Algorithm interview questions test your foundational knowledge. For example, a common question like bias/variance tradeoff helps the interviewer know how deep your knowledge of the concept truly is, as well as your ability to communicate complex ideas.

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    How Can Outlier Values Be Treated

    You can drop outliers only if it is a garbage value.

    Example: height of an adult = abc ft. This cannot be true, as the height cannot be a string value. In this case, outliers can be removed.

    If the outliers have extreme values, they can be removed. For example, if all the data points are clustered between zero to 10, but one point lies at 100, then we can remove this point.

    If you cannot drop outliers, you can try the following:

    • Try a different model. Data detected as outliers by linear models can be fit by nonlinear models. Therefore, be sure you are choosing the correct model.
    • Try normalizing the data. This way, the extreme data points are pulled to a similar range.
    • You can use algorithms that are less affected by outliers an example would be random forests.

    Learn Data Science with R for FREE

    What Do You Understand By Selection Bias In Machine Learning

    Read Machine Learning Interview Questions Online by Tech Interviews

    Selection bias stands for the bias which was introduced by the selection of individuals, groups or data for doing analysis in a way that the proper randomization is not achieved. It ensures that the sample obtained is not representative of the population intended to be analyzed and sometimes it is referred to as the selection effect. This is the part of distortion of a statistical analysis which results from the method of collecting samples. If you dont take the selection bias into the account then some conclusions of the study may not be accurate.

    The types of selection bias includes:

  • Sampling bias: It is a systematic error due to a non-random sample of a population causing some members of the population to be less likely to be included than others resulting in a biased sample.
  • Time interval: A trial may be terminated early at an extreme value , but the extreme value is likely to be reached by the variable with the largest variance, even if all variables have a similar mean.
  • Data: When specific subsets of data are chosen to support a conclusion or rejection of bad data on arbitrary grounds, instead of according to previously stated or generally agreed criteria.
  • Attrition: Attrition bias is a kind of selection bias caused by attrition discounting trial subjects/tests that did not run to completion.
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    What Is Supervised Learning

    Supervised learning is a machine learning algorithm of inferring a function from labeled training data. The training data consists of a set of training examples.

    Example: 01

    Knowing the height and weight identifying the gender of the person. Below are the popular supervised learning algorithms.

    • Support Vector Machines

    Apply Concepts And Work On Your Relevant Skills

    Throughout your interview, make sure to connect your answers with real-life examples, especially ones that reference your own work. Recruiters are usually looking for experience as well as knowledge, and the more experience you can demonstrate while discussing machine learning concepts, the more you’ll be able to highlight your preparedness for the job.

    Itâs also beneficial to show that youâre always learning and developing your skills. Show how driven you are to improve yourself and your expertise during the interview process. A recruiter may be impressed to see that youâre always striving to improve and grow.

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    What Do You Understand By Machine Learning

    Machine learning is the form of Artificial Intelligence that deals with system programming and automates data analysis to enable computers to learn and act through experiences without being explicitly programmed.

    For example, Robots are coded in such a way that they can perform the tasks based on data they collect from sensors. They automatically learn programs from data and improve with experiences.

    How Would You Handle An Imbalanced Dataset

    Google Machine Learning System Design Mock Interview

    Sampling Techniques can help with an imbalanced dataset. There are two ways to perform sampling, Under Sample or Over Sampling.

    In Under Sampling, we reduce the size of the majority class to match minority class thus help by improving performance w.r.t storage and run-time execution, but it potentially discards useful information.

    For Over Sampling, we upsample the Minority class and thus solve the problem of information loss, however, we get into the trouble of having Overfitting.

    There are other techniques as well Cluster-Based Over Sampling In this case, the K-means clustering algorithm is independently applied to minority and majority class instances. This is to identify clusters in the dataset. Subsequently, each cluster is oversampled such that all clusters of the same class have an equal number of instances and all classes have the same size

    Synthetic Minority Over-sampling Technique A subset of data is taken from the minority class as an example and then new synthetic similar instances are created which are then added to the original dataset. This technique is good for Numerical data points.

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    What Is Clustering In Machine Learning

    Clustering is a technique used in unsupervised learning that involves grouping data points. The clustering algorithm can be used with a set of data points. This technique will allow you to classify all data points into their particular groups. The data points that are thrown into the same category have similar features and properties, while the data points that belong to different groups have distinct features and properties. Statistical data analysis can be performed by this method. Let us take a look at three of the most popular and useful clustering algorithms.

    How Should You Maintain A Deployed Model

    The steps to maintain a deployed model are:


    Constant monitoring of all models is needed to determine their performance accuracy. When you change something, you want to figure out how your changes are going to affect things. This needs to be monitored to ensure it’s doing what it’s supposed to do.


    Evaluation metrics of the current model are calculated to determine if a new algorithm is needed.


    The new models are compared to each other to determine which model performs the best.


    The best performing model is re-built on the current state of data.

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    How Do You Build A Random Forest Model

    A random forest is built up of a number of decision trees. If you split the data into different packages and make a decision tree in each of the different groups of data, the random forest brings all those trees together.

    Steps to build a random forest model:

  • Randomly select ‘k’ features from a total of ‘m’ features where k < < m
  • Among the ‘k’ features, calculate the node D using the best split point
  • Split the node into daughter nodes using the best split
  • Repeat steps two and three until leaf nodes are finalized
  • Build forest by repeating steps one to four for ‘n’ times to create ‘n’ number of trees
  • What’s The Difference Between Random Forest And Gradient Boosting Algorithms

    Machine Learning Engineer: Must

    Random forests and gradient boosting algorithms are learning techniques you can use for classification and regression problems. This question measures your ability to apply tree-based algorithms to machine learning problems. You can respond by explaining the fundamental differences between the two methods.

    Example:”Random forest algorithms use bagging algorithms. Bagging combines different independent models and averages their prediction. Merging models can help lower variance. You can apply gradient boosting to convert weak learners to stronger ones. A weak learner can be a function whose performance is slightly better than random. You can create stronger learners from a weak model by taking its predictions and placing more weight on the misclassified samples. This weighting produces a new dataset that you can then use to train a better model. “

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    Explain How You Would Develop A Data Pipeline

    Data pipelines enable us to take a data science model and automate or scale it. A common data pipeline tool is Apache Airflow, and Google Cloud, Azure, and AWS are used to host them.

    For a question like this, you want to explain the required steps and discuss real experience you have building data pipelines.

    The basic steps are as follows for a Google Cloud host:

  • Sign into Google Cloud Platform
  • Create a compute instance
  • Pull tutorial contents from GitHub
  • Use AirFlow for an overview of the pipeline
  • Use Docker to set up virtual hosts
  • Develop a Docker container
  • Open Airflow UI and run the ML pipeline
  • Run the deployed web app
  • Q18explain Ensemble Learning Technique In Machine Learning

    Ensemble Learning Machine Learning Interview Questions Edureka

    Ensemble learning is a technique that is used to create multiple Machine Learning models, which are then combined to produce more accurate results. A general Machine Learning model is built by using the entire training data set. However, in Ensemble Learning the training data set is split into multiple subsets, wherein each subset is used to build a separate model. After the models are trained, they are then combined to predict an outcome in such a way that the variance in the output is reduced.

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    How Can You Avoid Overfitting Your Model

    Overfitting refers to a model that is only set for a very small amount of data and ignores the bigger picture. There are three main methods to avoid overfitting:

  • Keep the model simpletake fewer variables into account, thereby removing some of the noise in the training data
  • Use cross-validation techniques, such as k folds cross-validation
  • Use regularization techniques, such as LASSO, that penalize certain model parameters if they’re likely to cause overfitting
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