**Machine Learning Interview Questions and Answers, Machine Learning Interview Questions and Answers Freshers, Machine Learning Interview Questions and Answers, Machine Learning Interview Questions**

Before getting on to the **Machine Learning interview questions**, the student must know that the Machine Learning is a continuously varying field which needs the students as well as professionals to upgrade their skills with the new features and knowledge, to get fit for the jobs associated with Machine Learning. This post related to **Machine Learning Interview Questions and Answers, Machine Learning Interview Questions and Answers Freshers, Machine Learning Interview Questions and Answers, Machine Learning Interview Questions** will help you let out find all the solutions that are frequently asked in you upcoming Machine Learning interview.

Over thousands of vacancies available for the Machine Learning developers, experts must be acquaintance with all the component of Machine Learning technologies. This is necessary for the students in order to have in-depth knowledge of the subject so that they can have best employment opportunities in the future. Knowing every little detail about Machine Learning is the best approach to solve the problems linked with problem.

APTRON has spent hours and hours in researching about the **Machine Learning Interview Questions and Answers, Machine Learning Interview Questions and Answers Freshers, Machine Learning Interview Questions and Answers, Machine Learning Interview Questions** that you might encounter in your upcoming interview. All these questions will alone help you to crack the interview and make you the best among all your competitors.

First of all, let us tell you about how the Machine Learning technology is evolving in today’s world and how demanding it is in the upcoming years. In fact, according to one study, most of the companies and businesses have moved to the Machine Learning. Now, you cannot predict how huge the future is going to be for the people experienced in the related technologies.

Hence, if you are looking for boosting up your profile and securing your future, Machine Learning will help you in reaching the zenith of your career. Apart from this, you would also have a lot of opportunities as a fresher.

These questions alone are omnipotent. Read and re-read the questions and their solutions to get accustomed to what you will be asked in the interview. These Machine Learning interview questions and answers will also help you on your way to mastering the skills and will take you to the giant world where worldwide and local businesses, huge or medium, are picking up the best and quality Machine Learning professionals.

This ultimate list of best Machine Learning interview questions will ride you through the quick knowledge of the subject and topics like Machine Learning Techniques, System Design, Supervised Learning, Regression. This Machine Learning interview questions and answers can be your next gateway to your next job as a Machine Learning expert.

**These are very Basic Machine Learning Interview Questions and Answers for freshers and experienced both.**

Q1: What is Machine learning?

**A1:** Machine learning is a field of computer science that deals with system programming to learn and improve with experience.

For example: Robots are coded so that they can perform the task based on data they collect from sensors. It robotically learns programs from data.

Q2: What is ‘Training set’ and ‘Test set’?

**A2: Training set:** It is a set of data is used to discover the potentially predictive relationship in various areas of information science like machine learning. It is an example given to the learner.

**Test set:** It is used to test the accuracy of the hypotheses generated by the learner, and it is the set of example held back from the learner.

Q3: Explain what is the function of ‘Unsupervised Learning’?

**A3:**

- Find clusters of the data
- Find low-dimensional representations of the data
- Find interesting directions in data
- Interesting coordinates and correlations
- Find novel observations/ database cleaning

Q4: What is Genetic Programming?

**A4:** Genetic programming is one of the two techniques used in machine learning. The model is based on the testing and selecting the best choice among a set of results.

Q5: What is the difference between artificial learning and machine learning?

**A5: Machine Learning:** Designing and developing algorithms according to the behaviors based on empirical data are known as Machine Learning.

**Artificial intelligence:** in addition to machine learning, it also covers other aspects like knowledge representation, natural language processing, planning, robotics etc.

Q6: Why overfitting happens?

**A6:** The possibility of overfitting happens as the criteria used for training the model is not the same as the criteria used to judge the efficiency of a model.

Q7: What is the standard approach to supervised learning?

**A7:** Split the set of example into the training set and the test is the standard approach to supervised learning is.

Q8: In what areas Pattern Recognition is used?

**A8:** Pattern Recognition can be used in the following areas:

- Computer Vision
- Data Mining
- Speech Recognition
- Informal Retrieval
- Statistics
- Bio-Informatics

Q9: What is Model Selection in Machine Learning?

**A9:** The process of choosing models among diverse mathematical models, which are used to define the same data set is known as Model Selection. It is applied to the fields of statistics, data mining and machine learning.

Q10: What is deep learning?

**A10:** This might or might not apply to the job you’re going after, but your answer will help to show you know more than just the technical aspects of machine learning. Deep learning is a subset of machine learning. It refers to using multi-layered neural networks to process data in increasingly complex ways, enabling the software to train itself to perform tasks like speech and image recognition through exposure to these vast amounts of data. Thus the machine undergoes continual improvement in the ability to recognize and process information. Layers of neural networks stacked on top of each for use in deep learning are called deep neural networks.

Q11: How do you choose an algorithm for a classification problem?

**A11:** The answer depends on the degree of accuracy needed and the size of the training set. If you have a small training set, you can use a low variance/high bias classifier. If your training set is large, you will want to choose a high variance/low bias classifier.

Q12: How do bias and variance play out in machine learning?

**A12:** Both bias and variance are errors. Bias is an error due to flawed assumptions in the learning algorithm. Variance is an error resulting from too much complexity in the learning algorithm.

Q13: What is kernel SVM?

**A13:** Kernel SVM is the abbreviated version of kernel support vector machine. Kernel methods are a class of algorithms for pattern analysis and the most common one is the kernel SVM.

Q14: What is a recommendation system?

**A14:** Anyone who has used Spotify or shopped at Amazon will recognize a recommendation system: It’s an information filtering system that predicts what a user might want to hear or see based on choice patterns provided by the user.

Q15: What is ‘Overfitting’ in Machine learning?

**A15:** In machine learning, when a statistical model describes random error or noise instead of underlying relationship ‘overfitting’ occurs. When a model is excessively complex, overfitting is normally observed, because of having too many parameters with respect to the number of training data types. The model exhibits poor performance which has been overfit.

Q16: What is inductive machine learning?

**A16:** The inductive machine learning involves the process of learning by examples, where a system, from a set of observed instances tries to induce a general rule.

Q17: List down various approaches for machine learning?

**A17:** The different approaches in Machine Learning are

a) Concept Vs Classification Learning

b) Symbolic Vs Statistical Learning

c) Inductive Vs Analytical Learning

Q18: What is algorithm independent machine learning?

**A18:** Machine learning in where mathematical foundations is independent of any particular classifier or learning algorithm is referred as algorithm independent machine learning?

Q19: What is Genetic Programming?

**A19:** Genetic programming is one of the two techniques used in machine learning. The model is based on the testing and selecting the best choice among a set of results.

Q20: Explain the two components of Bayesian logic program?

**A20:** Bayesian logic program consists of two components. The first component is a logical one ; it consists of a set of Bayesian Clauses, which captures the qualitative structure of the domain. The second component is a quantitative one, it encodes the quantitative information about the domain.

Q21: How is KNN different from K-means clustering?

**A21:** KNN stands for K- Nearest Neighbours, it is classified as a supervised algorithm.

K-means is an unsupervised cluster algorithm.

Q22: Explain what is precision and Recall?

**A22: Recall:**

It is known as a true positive rate. The number of positives that your model has claimed compared to the actual defined number of positives available throughout the data.

**Precision:**

It is also known as a positive predicted value. This is more based on the prediction. It is a measure of a number of accurate positives that the model claims when compared to the number of positives it actually claims.

Q23: What is the difference between Type 1 and Type 2 errors?

**A23:** Type 1 error is classified as a false positive. I.e. This error claims that something has happened but the fact is nothing has happened. It is like a false fire alarm. The alarm rings but there is no fire.

Type 2 error is classified as a false negative. I.e. This error claims that nothing has happened but the fact is that actually, something happened at the instance.

The best way to differentiate a type 1 vs type 2 error is:

Calling a man to be pregnant- This is Type 1 example

Calling pregnant women and telling that she isn’t carrying any baby- This is type 2 example

Q24: What is the F1 score?

**A24:** The F1 score is defined as a measure of a model’s performance.

Q25: Pick an algorithm and write a Pseudocode for the same?

**A25:** This question depicts your understanding of the algorithm. This is something that one has to be very creative and also should have in-depth knowledge about the algorithms and first and foremost the individual should have a good understanding of the algorithms. Best way to answer this question would be start off with Web Sequence Diagrams.

Q26: Define a hash table?

**A26:** They are generally used for database indexing.

A hash table is nothing but a data structure that produces an associative array.

Q27: What is the difference between inductive machine learning and deductive machine learning?

**A27:** In inductive machine learning, the model learns by examples from a set of observed instances to draw a generalized conclusion whereas in deductive learning the model first draws the conclusion and then the conclusion is drawn. Let’s understand this with an example, for instance, if you have to explain to a kid that playing with fire can cause burns. There are two ways you can explain this to kids, you can show them training examples of various fire accidents or images with burnt people and label them as “Hazardous”. In this case the kid will learn with the help of examples and not play with fire. This is referred to as Inductive machine learning. The other way is to let your kid play with fire and wait to see what happens. If the kid gets a burn they will learn not to play with fire and whenever they come across fire, they will avoid going near it. This is referred to as deductive learning.

Q28: Why is Naïve Bayes machine learning algorithm naïve?

**A28:** Naïve Bayes machine learning algorithm is considered Naïve because the assumptions the algorithm makes are virtually impossible to find in real-life data. Conditional probability is calculated as a pure product of individual probabilities of components. This means that the algorithm assumes the presence or absence of a specific feature of a class is not related to the presence or absence of any other feature (absolute independence of features), given the class variable. For instance, a fruit may be considered to be a banana if it is yellow, long and about 5 inches in length. However, if these features depend on each other or are based on the existence of other features, a naïve Bayes classifier will assume all these properties to contribute independently to the probability that this fruit is a banana. Assuming that all features in a given dataset are equally important and independent rarely exists in the real-world scenario.

Q29: List out some important methods of reducing dimensionality.

**A29:**

- Combine features with feature engineering.
- Use some form of algorithmic dimensionality reduction like ICA or PCA.
- Remove collinear features to reduce dimensionality.

Q30: What kind of problems does regularization solve?

**A30: **Regularization is used to address overfitting problems as it penalizes the loss function by adding a multiple of an L1 (LASSO) or an L2 (Ridge) norm of your weights vector w.

Q31: What are parametric models? Give an example.

**A31:** Parametric models are those with a finite number of parameters. To predict new data, you only need to know the parameters of the model. Examples include linear regression, logistic regression, and linear SVMs.

Non-parametric models are those with an unbounded number of parameters, allowing for more flexibility. To predict new data, you need to know the parameters of the model and the state of the data that has been observed. Examples include decision trees, k-nearest neighbors, and topic models using latent dirichlet analysis.

Q32: Explain the Bias-Variance Tradeoff.

**A32:** Predictive models have a tradeoff between bias (how well the model fits the data) and variance (how much the model changes based on changes in the inputs).

Simpler models are stable (low variance) but they don’t get close to the truth (high bias).

More complex models are more prone to being overfit (high variance) but they are expressive enough to get close to the truth (low bias).

The best model for a given problem usually lies somewhere in the middle.

Q33: What is the Box-Cox transformation used for?

**A33:** The Box-Cox transformation is a generalized “power transformation” that transforms data to make the distribution more normal.

For example, when its lambda parameter is 0, it’s equivalent to the log-transformation.

It’s used to stabilize the variance (eliminate heteroskedasticity) and normalize the distribution.

Q34: Explain Latent Dirichlet Allocation (LDA).

**A34:** Latent Dirichlet Allocation (LDA) is a common method of topic modeling, or classifying documents by subject matter.

LDA is a generative model that represents documents as a mixture of topics that each have their own probability distribution of possible words.

The “Dirichlet” distribution is simply a distribution of distributions. In LDA, documents are distributions of topics that are distributions of words.

Q35: You are given a data set. The data set has missing values which spread along 1 standard deviation from the median. What percentage of data would remain unaffected? Why?

**A35:** This question has enough hints for you to start thinking! Since, the data is spread across median, let’s assume it’s a normal distribution. We know, in a normal distribution, ~68% of the data lies in 1 standard deviation from mean (or mode, median), which leaves ~32% of the data unaffected. Therefore, ~32% of the data would remain unaffected by missing values.

Q36: When is Ridge regression favorable over Lasso regression?

**A36:** You can quote ISLR’s authors Hastie, Tibshirani who asserted that, in presence of few variables with medium / large sized effect, use lasso regression. In presence of many variables with small / medium sized effect, use ridge regression.

Conceptually, we can say, lasso regression (L1) does both variable selection and parameter shrinkage, whereas Ridge regression only does parameter shrinkage and end up including all the coefficients in the model. In presence of correlated variables, ridge regression might be the preferred choice. Also, ridge regression works best in situations where the least square estimates have higher variance. Therefore, it depends on our model objective.

Q37: What is the difference between covariance and correlation?

**A37:** Correlation is the standardized form of covariance.

Covariances are difficult to compare. For example: if we calculate the covariances of salary ($) and age (years), we’ll get different covariances which can’t be compared because of having unequal scales. To combat such situation, we calculate correlation to get a value between -1 and 1, irrespective of their respective scale.

Q38: What is convex hull ? (Hint: Think SVM)

**A38:** In case of linearly separable data, convex hull represents the outer boundaries of the two group of data points. Once convex hull is created, we get maximum margin hyperplane (MMH) as a perpendicular bisector between two convex hulls. MMH is the line which attempts to create greatest separation between two groups.

Q39: What’s a Fourier transform?

**A39:** A Fourier transform is a generic method to decompose generic functions into a superposition of symmetric functions. Or as this more intuitive tutorial puts it, given a smoothie, it’s how we find the recipe. The Fourier transform finds the set of cycle speeds, amplitudes and phases to match any time signal. A Fourier transform converts a signal from time to frequency domain — it’s a very common way to extract features from audio signals or other time series such as sensor data.

Q40: What’s the difference between a generative and discriminative model?

**A40:** A generative model will learn categories of data while a discriminative model will simply learn the distinction between different categories of data. Discriminative models will generally outperform generative models on classification tasks.

Q41: How would you evaluate a logistic regression model?

**A41:** A subsection of the question above. You have to demonstrate an understanding of what the typical goals of a logistic regression are (classification, prediction etc.) and bring up a few examples and use cases.

**Machine Learning Conclusion Interview FAQs**

We know the list of

However, you will be asked with the questions in the interview related to the above mentioned questions. Preparing and understanding all the concept of Machine Learning technology will help you strengthen the other little information around the topic.

After preparing these interview questions, we recommend you to go for a mock interview before facing the real one. You can take the help of your friend or a Machine Learning expert to find the loop holes in your skills and knowledge. Moreover, this will also allow you in practicing and improving the communication skill which plays a vital role in getting placed and grabbing high salaries.

Remember, in the interview, the company or the business or you can say the examiner often checks your basic knowledge of the subject. If your basics is covered and strengthened, you can have the job of your dream. The industry experts understand that if the foundation of the student is already made up, it is easy for the company to educate the employ towards advance skills. If there are no basics, there is no meaning of having learnt the subject.

Therefore, it’s never too late to edge all the basics of any technology. If you think that you’ve not acquired the enough skills, you can join our upcoming batch of Machine Learning Training in Noida. We are one of the best institute for Machine Learning in noida which provide advance learning in the field of Machine Learning Course. We’ve highly qualified professionals working with us and promise top quality education to the students.

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