Advanced Machine Learning Interview Questions

If you're looking for Machine Learning Interview Questions for Freshers and Experienced, you are in the right place. There are a lot of opportunities from many reputed companies in the world. According to research Machine Learning has a market size of about USD 3,682 Million by 2021. So, You still have the opportunity to move ahead in your career in Machine Learning Development. Mindmajix offers Advanced Machine Learning Interview Questions 2021 that helps you in cracking your interview & acquire dream career as Machine Learning Developer.

Q1) What is the difference between Bias and Variance?

Ans: Bias:
Bias can be defined as a situation where an error has occurred due to the use of assumptions in the learning algorithm.

Variance:
Variance is an error caused because of the complexity of the algorithm that is been used to analyze the data.

Q2) What is the difference between supervised and unsupervised machine learning?

Ans: Supervised learning is a process where it requires training labeled data. When it comes to Unsupervised learning it doesn’t require data labeling.

Q3) What are the three stages of model building in the machine learning?
Ans: Following are the three stages of model building:

Model Building

In this stage, we will choose the ideal algorithm for the model, and we will train it based on our requirement.

Model Testing

In this stage, we will check the model accuracy by using test data.

Applying Model

After testing, we have to make the changes, and then we can use the model for the real-time projects.

Q4) What are the applications of supervised machine learning?
Ans: Following are the applications of machine learning:

Fraud Identification

Supervised learning trains the model for identifying the suspicious patterns; we can identify the feasible fraud instances.

Healthcare

By giving the images about a disease, supervised machine learning can train the model for detecting whether a person is affecting from illness or not.

Email spam identification

We train the model through historical data which contains emails that are classified as spam or not spam. This labeled data is supplied as the input to the model.

Sentiment Analysis

This relates to the process of using algorithms for mining the documents and determining if they are negative, neutral, positive in sentiment.

Q5) What are the techniques of Unsupervised machine learning?
Ans: Following are the different techniques of unsupervised machine learning:

Clustering

It includes the data that must be divided into the subsets. These subsets are also known as clusters. Diverse clusters disclose details about objects, unlike regression or classification.

Association

In the association problem, we can recognise the association patterns between different items and variables. For instance, the e-commerce can indicate other items for us to buy according to our previous purchases.

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