Machine Learning Interview Questions and Answer for 2021
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Machine Learning Interview Questions and Answer for 2021

Machine Learning Interview Questions and Answer for 2021

Since the introduction of Artificial Intelligence, Machine Learning, and Deep Learning, the world has changed and will continue to evolve in the years ahead. We’ve included the most often asked questions by interviewers in this machine learning training Interview Questions in 2021.

Our focus will be on real-world ML interview questions asked by Microsoft, Amazon, and other companies, as well as how to respond to them.

Let’s get this party started!

To begin, Machine Learning is the process of teaching computer software to create a statistical model from data. The purpose of machine learning (ML) is to transform data and extract essential patterns or insights from it.

Interview questions that are important for machine learning for beginners

Why is the Machine Learning trend gaining traction so quickly?

Machine Learning is used to solve problems in the real world. Machine learning algorithms learn from data, as opposed to using a hard coding rule to solve a problem.

Later on, the learned information can be utilized to anticipate the feature. Early adopters are reaping the benefits.

A whopping 82 percent of businesses that have implemented machine learning and artificial intelligence (AI) have seen a considerable return on their investment.

Companies have an amazing median ROI of 17 percent, according to Deloitte.

What different forms of Machine Learning are there?

Machines can learn in three different ways:

Supervised Learning

Unsupervised Learning

Reinforcement Learning

Supervised learning-

Supervised learning is a type of machine learning that employs labeled data.

It’s like if you’re learning under the supervision of an instructor.

The training dataset functions as a tutor for the machine.

When making choices with new data, the model is trained on a pre-defined dataset.

Unsupervised Learning:

Unsupervised learning is a type of machine learning that involves training the system on unlabeled data or without any assistance.

Learning without the help of a teacher.

The model learns from its observations and looks for patterns in the data.

The model is given a dataset and told to create clusters to automatically detect patterns and relationships in it.

Reinforcement Learning:

Reinforcement learning is a type of learning in which an agent interacts with its surroundings by performing actions and discovering faults or rewards.

It’s like being stranded on a deserted island, where you must explore the area on your own and learn to live and adapt to the harsh surroundings.

The hit-and-trial strategy is used by the model to learn.

It learns by receiving a reward or a penalty for each action it does.

What would you say to a school-aged child about Machine Learning?

Suppose you go to a carnival and come across a lot of people. Because you don’t know who they are, you’ll mentally categorize them by gender, age group, clothing, and so on.

Strangers represent unlabeled data in this instance, and the process of classifying unlabeled data pieces is nothing more than unsupervised learning.

This becomes an unsupervised learning challenge because you didn’t utilize any prior knowledge about people and classed them on the fly.

What is the difference between Deep Learning and Machine Learning?

Deep Learning is a type of machine learning that is inspired by the human brain’s structure and is particularly good at detecting features.

Machine Learning is made up of algorithms that read data, learn from it, and then use what they’ve learned to make better decisions.

What exactly do you mean when you say “selection bias”?

A bias in the sampling portion of an experiment is caused by statistical inaccuracy.

Because of the inaccuracy, one sampling group is chosen more frequently than the other groups in the experiment.

If the selection bias is not identified, it may lead to an incorrect conclusion.

What does the ROC curve indicate and what does it mean?

One of the fundamental tools for diagnostic test evaluation is the Receiver Operating Characteristics curve or ROC curve. It is a plot of sensitivity (true positive rate) against Specificity (false positive rate) for the many possible cut-off points of a diagnostic testĀ 

It demonstrates the sensitivity-specificity tradeoff (any increase in sensitivity will be accompanied by a decrease in specificity).

The test is more accurate if the curve follows the left-hand border and subsequently the top border of the ROC space.

If the curve touches the 45 degree diagonal mark, the test become less accurate.

The likelihood ratio (LR) for that value of the test is determined by the slope of the tangent line at a cutpoint.

The area under the curve (AUC) is a metric for test precision.

Is it better to have a large number of false positives or a large number of false negatives? Explain.

It relies on the topic as well as the domain for which the problem is being solved. If you’re employing Machine Learning in the field of medical testing, a false negative is a big risk, because the report won’t reveal any health issues even if the person is sick. Similarly, if Machine Learning is used to detect spam, a false positive is extremely dangerous since the algorithm may mistakenly classify a critical email as spam.

Model accuracy or model performance – which is more essential to you?

You should be aware that model accuracy is only a small part of overall model performance. The model’s accuracy and performance are directly proportional, therefore the greater the model’s performance, the more accurate the forecasts are.

In a Decision Tree, what is the difference between Gini Impurity and Entropy?

The metrics used to decide how to split a Decision Tree are Gini Impurity and Entropy.

The Gini measurement is the likelihood of accurately classifying a random sample based on the distribution in the branch if you pick a label at random.

Entropy is a metric for calculating the absence of information. A split is used to calculate the Information Gain (difference in entropies). This measure aids in the reduction of output label uncertainty.

How areĀ  information gain and entropy different from each other?

Entropy is a measure of how jumbled up your data is. As you get closer to the leaf node, it gets smaller.

The decrease in entropy after a dataset is split on an attribute is used to calculate the Information Gain. As you get closer to the leaf node, it continues to rise.

Wrapping up

The questions given above are the fundamentals of machine learning. Because machine learning is progressing at such a rapid pace, new concepts will arise. So join communities, go to conferences, and read research papers to stay up to date. You will be able to pass any ML interview if you do so. Enrolling in machine learning training and getting a machine learning certification will surely put you in a better position to grab the best jobs.