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- Top 20 Machine Learning Interview Questions & Answers

When you are planning to appear for the data science interview, machine learning is an important part of the process that could help you in becoming successful data scientists, machine learning engineers, or data engineers etc. **JanBask Training** created a free guide to data science interviews so that you can know exactly where you stand currently. Here, in this blog for machine leaning interview questions, we have added answers as well after a careful research and analysis.

In this blog, first, you will study machine learning interview questions related to algorithms and theory behind the machine learning. Further, you will study questions related to the programming and your general interest in the machine learning. So, are you ready to test your skills? Check the list of industry-specific questions below and take your career forward with the right process and approach.

- Machine Learning Interview Questions for Data Engineers
- Machine Learning Interview Questions for Data Scientists
- Machine Learning Interview Questions Answers for Freshers
- Machine Learning Interview Questions Answers for Experienced

- What is Bias Error in machine learning algorithm?
- What do you understand by Variance Error in machine learning algorithm?
- What is the bias-variance trade-off?
- How will you differentiate the supervised and unsupervised machine learning?
- How is the k-nearest algorithm different from the KNN clustering?
- What is ROC (Receiver operating characteristic) Curve? Explain the working of ROC.
- What do you mean by the precision and the recall?
- What is the significance of Bayes’ theorem in the context of the machine learning algorithm?
- What is Naïve Bayes in machine learning?
- How will you differentiate the L1 and L2 regularization?

- What is your favorite algorithm? Explain in less than a minute based on your past experiences.
- Have you ever worked on type 1 or Type 2 errors?
- How will you explain the Fourier Transformation in Machine Learning?
- How will you differentiate the machine learning and deep learning algorithms?
- How will you differentiate the generic model from the discriminative model?
- What seems more important either model accuracy or performance of a model?
- What is the F1 score and explain its uses too?
- Is it possible to manage imbalanced datasets in machine learning?
- Why is classification better than regression for machine learning experts?
- How would you check the effectiveness of machine learning model?

Bias is the common error in the machine learning algorithm due to simplistic assumptions. It may undermine your data and does not allow you to achieve maximum accuracy. Further generalizing the knowledge from the training set to the test sets would be highly difficult for you.

Variance error is common in machine learning when the algorithm is highly complex and difficult to understand as well. It may lead high degree of variation to your training data that can lead the model to overfit the data. Also, there could be so much noise for the training data that is not necessary in case of the test data.

The bias-variance trade-off is able to handle the learning errors effectively and manages noise too that happens due to underlying data, Essentially, this trade-off will make the model more complex than usual but errors are reduced optimally.

Supervised learning needs data in the labeled form. For example, if you wanted to classify the data then you should first label the data then classify it into groups. On the other hand, unsupervised does not need any data labeling explicitly.

K-nearest algorithm is the supervised learning while the k-means algorithm is assigned under the unsupervised learning. While these two techniques look similar at the first glance, still there is a lot of difference between the two. Supervised learning needs data in the labeled form.

For example, if you wanted to classify the data then you should first label the data then classify it into groups. On the other hand, unsupervised does not need any data labeling explicitly. The application of both the techniques depends on project needs.

A ROC curve is the pictorial representation of the contrast between true positive rates and the false positive rates calculated at multiple thresholds. It is used as the proxy to measure the trade-offs and sensitivity of the model. Based on the observation, it will trigger the false alarms.

The Recall is the measure of true positive rates claimed against the total number of datasets. Precision is the prediction of positive values that your model claims compared to the number of positives it actually claims. It can be taken a special case of probability as well in case of mathematics.

With the Bayes’ Theorem, you could measure the posterior probability of an event based on your prior knowledge. It will tell you the true positive rate of a condition when divided by the sum of total false rates.

Bayes Theorem is also named as the Bayes Rule in mathematics, and it is popular for calculating the conditional probability. The name of the theorem was given after a popular mathematician Thomas Bayes. The concept of Bayes theorem is confusing sometimes but a depth understanding helps you to gain meaningful insights over the topic.

Naïve is the word used to define the things that are virtually impossible in the real-life. Here, also you need to calculate the conditional probability as the pure product of individual probabilities of different components. This is the absolute condition that could never meet in the real-life. Have you ever heard of a pickle ice cream in actual?

L2 regularization trends to spread error among multiple terms while L! is more specific to binary variables where either 0 or 1 is assigned based on requirements. L1 tends to set a Laplacian prior on terms, but L2 tends to settings a Gaussian prior on terms.

The answer to this question will vary based on the projects you worked on earlier. Also, which algorithm assured better outcomes as compared to other.

This is a tricky question usually asked by experienced candidates only. If you would be able to answer this question then make sure that you are at the top of the game. Type 1 error is the false positive and Type 2 error is a false negative. Type 1 error signifies something has happened even if it does not exist in real while Type 2 error means you claim something is happening in real.

A Fourier Transformation is the generic method that helps in decomposing functions into a series of symmetric functions. It helps you in finding the set of cycle speeds, phases, and amplitude to match the particular time signal. It has the capability to convert the signal into frequency domain like sensor data or more.

The deep learning is a part of machine learning that is usually connected with the neural networks. This is a popular technique from neuroscience to model a set of labeled and structured data more precisely. In brief, deep learning is an unsupervised learning algorithm that represents data with the help of neural nets.

A generic model will explain the multiple categories of data while the discriminative model simply tells the difference between data categories. They are used in classification tasks and need to understand deeply before you actually implement them.

Well, model accuracy is just a subset of the model performance parameter. For a model who is performing excellent, there are chances of more accuracy than others.

The F1 score is used to check the performance of a model or this is the average of precision and recall of a model where 1 means the best and 0 means the worst.

Collect more data, manage the imbalanced data, try a different algorithm to work on imbalanced datasets in machine learning.

Classification gives you discrete results while regression works on continuous results more. To become more specific with data points, you are always recommended using classification over regression in machine learning.

For this purpose, you can always check the F1 score to make sure either machine learning model is working effectively or needs improvement.

*All the best and Happy job hunting!*

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