10 Frequently Asked Interview Questions For Machine Learning In 2019

At the present scenario, organisations are hiring machine learning professionals to build applications with the help of machine learning techniques. The impact of this emerging technology is globally concerned. In this article, we list down 10 frequently asked questions to prepare for machine learning interviews in 2019.

1| Difference Between Supervised And Unsupervised Machine Learning

In Supervised machine learning technique, the machine is trained by using labelled data. Then a new dataset is fed into the learning model so that the algorithm gives a positive outcome by analysing the labelled data. On the other hand, in the unsupervised machine learning technique, the machine is not trained using labelled data and let the algorithms make decisions without any corresponding output variables.   Know more here.

2| Difference between KNN and K-means

KNN or K nearest neighbours is a supervised algorithm used for classification where a test sample is given as the class of the majority of its nearest neighbours. While K-means is an unsupervised algorithm mainly used for clustering. It works by considering a group of unlabelled points and grouping them into “k” number of clusters. Know more here.

3| Difference between machine learning and deep learning

Machine learning is a branch of computer science and a method to implement artificial intelligence. This technique provides the ability to automatically learn and improve from experiences without being explicitly programmed. Deep learning can be said as a subset of machine learning. It is mainly based on the artificial neural network where data is taken as an input and the technique makes intuitive decisions using the artificial neural network. Know more here.

4| How to handle missing data in a dataset?

Missing data is one of the common factors while working with data and handling it can be said as one of the greatest challenges faced by the data analysts. There are several ways one can impute the missing values. Some of the methods such as deleting rows, replacing with mean/median/mode, assigning a unique category, predicting the missing values, using algorithms which support missing values, etc. Know more here.

5| What do you mean by Overfitting? How to avoid this?

While training a model if the data is fed in a large amount, then the model starts learning from the noise and other inaccurate data from the dataset. As a result, it becomes difficult for the model to generalise new instances other than the training set.   There are several solutions which can be used to avoid overfitting such as using Cross-validation method, pruning, regularisation, etc. Know more here.

6| What do you mean by Inductive Logic Programming (ILP)?

Inductive  Logic Programming is a subfield of machine learning which uses logic programming and aims at searching patterns in data which can be used to build predictive models. In this process, the logic programs are assumed as a background knowledge or considered as a hypothesis. Know more here.

7| Difference between Classification and Regression

Both classification and regression are a part of supervised machine learning techniques. In classification methods, the predictions are done by classifying the output into various categories. The regression models are generally used for finding out the relationship between variables and forecasting. The foremost difference between these two methods is that the output variable in classification is discrete while for the regression it is continuous. Know more here.

8|  What is Ensemble Learning?

The learning algorithms which construct a set of classifiers and then classify new data points by taking a choice of their predictions are known as Ensemble methods.  The ensemble methods, also known as committee-based learning or learning multiple classifier systems train multiple hypotheses to solve the same problem. One of the most common examples of ensemble modelling is the random forest trees where a number of decision trees are used to predict outcomes. Know more here.

9| What Are The Steps Involved In Machine Learning Project?

As you plan for doing a machine learning project. There are several important steps you must follow to achieve a good working model and they are data collection, data preparation, choosing a machine learning model, training the model, model evaluation, parameter tuning and lastly prediction. Know more here.  

10|  What Do You Mean By Precision And Recall?

These two are the measures which are used in the information retrieval domain in order to measure how good an information retrieval system reclaims the related data as requested by the user. Precision can be said as positive predictive value is the fraction of relevant instances among the retrieved instances while recall, also known as sensitivity is the fraction of relevant instances which have been retrieved over the total amount of relevant instances. Know more here.