Machine Learning Interview Questions and Answers For Freshers

Machine Learning is the process of training a computer program to build a statistical model based on data. The goal of ML is to transform data and identify the key patterns and get insights. Machine learning has become an emerging skill that has gained massive relevance in today’s world. Companies worldwide are constantly looking for graduates with proficiency in machine learning. This article will help prepare you for such openings by providing you with a list of frequently asked machine learning interview questions and answers.

Frequently Asked Machine Learning Interview Questions and Answers 

Here is a list of the most frequently asked machine learning interview questions and answers.

1. What are the different types of machine learning?

There are three types of machine learning.

• Supervised learning, in which a model makes predictions or decisions based on past or labeled data.
• Unsupervised Learning, in which a model can identify patterns, anomalies, and relationships in the input data. There is also no labeled data in unsupervised learning.
• Reinforcement Learning, in which the model can learn based on the rewards it received for its previous action.

2. What is overfitting?

Overfitting occurs when a model grasps the training set too well, subsequently taking up random fluctuations in the training data as concepts. Overfitting impact the model’s ability to generalize and doesn’t normally apply to new data. 

3. What is a ROC curve?

The graphical representation of the difference between true positive rates and the false positive rate at various thresholds is called a ROC curve. It’s used as a proxy for the trade-off of the true positives vs the false positives.

4. What are precision and recall?

Recall aka the true positive rate is the number of positives your model claims compared to the actual number of positives there are. Precision aka the positive predictive value, and it is a measure of the number of accurate positives your model claims compared to the number of positives it actually claims.

5. What is Bayes’ Theorem?

Bayes’ Theorem gives you the posterior probability of an event given what is known as prior knowledge. It’s expressed as the true positive rate of a condition sample divided by the sum of the false positive rate of the population and the true positive rate of a condition
These are but a few important machine learning interview questions and answers. The field of machine learning is a vast one and it’s an important skill to have should you wish to make it in the world of data. Praxis understands the importance of machine learning and the role it plays, and after careful consideration has curated a state-of-the-art PGP in Data Science. This program will equip you with all the tools necessary to make your mark as a proficient data scientist and our astounding placement records are just another testaments to why we can indeed be the ideal choice to help pursue your goals.

Also read, Machine Learning Books You Must Read In 2022

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