6 Most common AI & ML job interview questions you must prepare for

 6 Most common AI & ML job interview questions you must prepare for

Written by-- Ashish Jha

New technologies like artificial intelligence and machine learning are being used by businesses to make big data more worthy. The advancement of modern technologies usage has seen a rise in various sectors like banking, healthcare, manufacturing, telecommunication. A rise in demand is being seen for job roles in fields like data science, artificial intelligence and machine learning.

However, cracking a machine learning interview is not easy. Many of the big tech companies are expecting candidates to be extremely talented and technologically sound which is giving a surge to the need for machine learning engineers. We have enlisted some of the top questions of machine learning which will help you land your dream job:

1. What is Machine Learning?

One of the most frequently asked questions. In layman terms, machine learning is a method of data analysis that automates analytical model building. As a result of using this, one can learn from data, identify patterns and can even make decisions with minimal human involvement. If artificial intelligence mimics human abilities, Machine learning acts as a subset of AI which actually trains a machine about the learning process.

2. What is the difference between data mining and data learning?

Both of the concepts orbit around Big Data. As most of their utilities are related to large datasets which are often confused as the same thing. With the enormous amount of development taking place every day, Machine learning is being considered as a futuristic technology that is being used to study various things like design and develop algorithms which are helpful for computers as it gives them the capability to learn without being explicitly programmed. Whereas on the other hand, data mining functions are used to extract useful data from unstructured data, which helps businesses extract knowledge or unknown interesting patterns.

3. What is overfitting and what can be done to avoid it?

Overfitting is an extreme situation in machine learning and takes place when a machine learning model is well versed in a dataset, it basically takes up random fluctuations as concepts and fails to specify the content. Hence, these models safeguard themselves from applying the concept of new data. If fed with properly trained data it showcases 100% accuracy. However, when it is coached with test data things change and it results in an error and low efficiency. So, to escape overfitting, companies should opt for simple models that have lesser variables and parameters. And the companies should pay more attention to regularization and training processes.

4. What is dimension reduction in machine learning?

A process of reducing the size of the feature matrix. A lesser input dimension generally means fewer parameters or a simple structure in the machine learning model. Meanwhile when a machine learning model when equipped with too many degrees of freedom usually overfit the training dataset then dimension reduction is used to lower its chances. Dimension reduction in machine learning signifies the effort to reduce the number of columns in it. By following this method many companies are getting a better feature which is set either by uniting columns or by removing extra variables.

5. What is the error matrix in machine learning?

Confusion matrix, which is also known as error matrix is a designated table that is used to measure the precise performance of machine learning algorithm. Categorizing accuracy if one has an unequal number of observations in each class or if you have more than two classes in your dataset can be misleading. Whereas calculation of a confusion matrix can give you a better insight as to whether your classification model is getting right and the sort of error it is making in the process. However, the confusion matrix has two specific parameters i.e., actual and predictable but is mostly used in supervised and unsupervised learning.

6. How to handle an imbalanced dataset?

What is an imbalanced test? While you are taking a classification test and your 90% of data is in one class, that is called an imbalanced test which often leads to accuracy disruption. A biased accuracy of 90% has no predictive power on the other category of data. So, to prevent such a situation, companies can collect more data to balance the imbalanced dataset. These data can also resemble the dataset to correct the imbalances and try different algorithms. However, one should always be alert of the disruption an unbalanced dataset can cause.