Most Asked Machine Learning Interview Questions -

Most Asked Machine Learning Interview Questions with Answers

Are you looking for Machine Learning Interview Questions? Are you preparing for Machine Learning interview? This is the right place you have come. Here, we will guide and help you to enhance your Machine Learning skills, and be prepared for job.

Here, we are providing a good collection of real-world Machine Learning Interview questions which are generally asked in big companies such as Google, Microsoft, Facebook, Oracle, and Amazon etc. Each question has a perfectly written answer. So, let’s start.

Que 1) What do you understand by the term Machine Learning?

The term Machine learning is a specific form of Artificial Intelligence that deals with system programming and automates data analysis to make computers to learn and act through experiences without being explicitly programmed. For example, we have seen the extraordinary working of Robots in many movies surprisingly. Robots are programmed in such a way that they can perform the tasks based on the data they receive from sensors. They automatically learn programs from data and improve with experiences. That is the power of Machine Learning.

Que 2) Why is the Machine Learning going popular day by day and this trend emerging so fast?

Machine Learning is going popular day by day and this trend emerging so fast because of the following reasons:

Machine Learning is one of the most trending technologies because it solves Real-World problems effectively.
It works unlike the hard coding rule to solve the problem because the machine learning algorithms learn from the data. This learning can later be used to predict the future.
It is a game changer and paying off for early adopters. According to a survey, 82% of the enterprises adopting machine learning and Artificial Intelligence (AI) have gained a significant financial advantage from their investments.

Que 3) What is the key difference between inductive learning and deductive learning?

Following is the key differences between inductive learning and deductive learning:

Inductive Learning	Deductive Learning
Inductive learning is a method of using observations to draw conclusions.	Deductive learning is a method of using conclusions to form observations.
In simple words, we can say that, in inductive learning, the model learns by examples from a set of observed instances to draw a generalized conclusion.	In the deductive learning, the model first applies the conclusion, and then draws the observations.
Example: Suppose, we have to explain to a kid that playing with fire can cause burns. There are two ways to explain this to a kid: First, we can show examples as various fire accidents videos or images of burnt people and label them as “Hazardous”. In this case, a kid will understand with the help of examples and not play with the fire. This is the form of Inductive Machine Learning.	Another way to teach the same thing to the kid is to let the kid play with the fire and wait to see what happens. If the kid gets a burn, it will teach the kid not to play with fire and avoid going near to it. This is a real-life example of Deductive machine Learning.

Que 4) What are the different types of Machine Learning?

Machine Learning can be categorized in mainly three types:

Supervised Machine Learning
Unsupervised Machine Learning
Reinforcement Machine Learning

Supervised Machine Learning: In Supervised Machine Learning, a model creates predictions or decisions according to the past or labeled data. Labeled data is a set of data that are given tags or labels, and thus made more meaningful.

Unsupervised Machine Learning: In Unsupervised Machine Learning, we don’t have labeled data so, here, a model can identify patterns, anomalies, and relationships in the input data.

Reinforcement Machine Learning: In Reinforcement Machine Learning, the model can learn based on the rewards it got for its previous actions. Let’s understand it by an example.

Suppose an agent is working in an environment. The agent is provided a target to achieve. Every time the agent takes some action toward the target, it is given positive feedback. And, if the action taken from the agent is going away from the goal, the agent is given negative feedback.

Que 5) What is the key difference between supervised and unsupervised machine learning?

Both supervised and unsupervised machine learning are learning algorithms that are used in machine learning. The key difference between them is as follows:

Supervised machine learning algorithms require labeled data as input, for example, prediction of stock market prices. On the other hand, in unsupervised machine learning, we do not have labeled data, and we group the unlabeled data, for example, conducting market segmentation.