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Machine learning is one of the most widespread technologies today. This comprehensive blog covers some of the most frequently asked machine learning interview questions to help you review all the necessary concepts and skills to land your dream job.
This blog is specially designed for you to prepare thoroughly for the Machine Learning interview before the interview.
 
Here is a list of the top 20 Machine learning interview questions.
 

Machine Learning Interview Questions for Freshers

 

1. Why was machine learning introduced?


The most genuine answer is to make your life easier. In the earlier days of "intelligent" applications, many systems used hard-coded "if" and "else" decision rules to process data or modify user input. Imagine a spam filter whose task is to move relevant incoming email messages to the spam folder.
But with machine learning algorithms, we get enough information to learn and identify patterns from the data.
Unlike everyday problems, we don't need to write new rules for every situation in machine learning. We need to use the same workflow but a different data set.
 

2. What is PCA? When do you utilize it?

 
Principal component analysis (PCA) is most generally utilized for dimensionality reduction.
In this case, PCA calculates the variation in each variable (or column in the table). If there is a slight deviation, it throws the variable out.
Principal Component Analysis (PCA)
This makes it easier to visualize the data set. PCA is utilized in finance, neuroscience, and pharmacology.
This is advantageous as a preprocessing step, especially when there are linear correlations between features.
 

3. What are support vectors in SVM?

 
A Support Vector Machine (SVM) is an algorithm that endeavors to place a line (or plane or hyperplane) between different classes to maximize the distance from the line to the class points.
This way, he tries to find a robust separation between the classes. Support vectors are points on the edge of the dividing hyperplane.
 

4. What are the different kernels in SVM?


There are six kinds of kernels in SVM:
Linear kernel: utilized when the data is linearly separable.
 
Polynomial Kernel: When you have discrete data that does not have a natural notion of smoothness.
 
Radial Kernel: Create a decision boundary that separates two classes much better than a linear kernel.
 
Sigmoid kernel: utilized as an activation function for neural networks.
 

5. What is cross-validation?


Cross-validation divides all your data into three parts: training, testing, and validation data. The data is separated into k subsets, and the model is trained on k-1 of these datasets.
The final subset is kept for testing. This is accomplished for each of the subsets. This is k-fold cross-validation. Finally, the scores from all k-folds are averaged to produce a final score.
 

6. What is bias in machine learning?


Data skew tells us that there is an inconsistency in the data. Inconsistency can occur for several reasons, which are not mutually exclusive.
For example, to speed up the hiring process, a tech giant like Amazon built one engine where it will put 100 resumes, spit out the best five and hire them.
When the company realized that the software was not producing gender-neutral results, it was modified to remove this bias.
 

7. Explain the distinction between classification and regression?


Classification is used to obtain discrete results; classification is used to classify data into some specific categories.
For example, sorting emails into spam and non-spam types.
Whereas regression handles continuous data.
For example, predicting goods prices at a certain point in time.
Classification is used to predict the output of a cluster of classes.
Such as, Is it hot or cold tomorrow?
On the other hand, regression is used to predict the connection that the data represents.
For example: What is the temperature tomorrow?
 

8. What is clustering?


Clustering is the technique of clustering a set of objects into various groups. Things in the same cluster should be similar and different from things in other clusters.
There are several types of clustering:
"      Hierarchical clustering
"      K stands for clustering
"      Density-based clustering
"      Fuzzy clustering, etc.
 

9. How can you choose K for K-means Clustering?


There are two kinds of methods which include direct methods and statistical testing methods:
Direct methods: Includes elbow and silhouette
Statistical Test Methods: Has gap statistics.
When determining the optimal value of k, the silhouette is most often used.
 

10. How do you make sure which machine learning algorithm to use?


It totally depends on the dataset one has. If the data is not continuous, we use SVM. If the data set is continuous, we use linear regression.
So there is no specific way to let us know which ML algorithm to use. It all comes down to exploratory data analysis (EDA).
EDA is like a "conversation" with a dataset; we do these things in EDA:
 
"      Classify our variables as persistent, categorical, and so on.
"      Summarize our variables using descriptive statistics.
"      Visualize our variables with graphs.
"      Based on the above observations, select the single most appropriate algorithm for a particular data set.
 

Advanced Machine Learning Interview Questions

 

11. How to deal with excessive and insufficient equipment?


Overfitting refers that the model fitting the training data too well. In this circumstance, we need to resample the data and evaluate the model's accuracy using techniques such as k-fold cross-validation.
While in the case of Underfitting, we cannot understand or capture patterns from the data, in this circumstance, we need to alter the algorithms or add more data points to the model.
 

12. What are referral systems?


A recommender is a system used to predict users' interests and recommend products that are likely to interest them.
The data required for recommendation systems comes from explicit user ratings after watching a movie or listening to a song, implicit search engine queries, purchase history, or another user/item knowledge.
 

13. How do you check the normality of a data set?


Visually, we can use graphs. Some of the normality checks are as follows:
"      Shapiro-Wilk test
"      Anderson-Darling test
"      Martinez-Iglewicz test
"      Kolmogorov-Smirnov test
"      D'Agostino skewness test
 

14. Can logistic regression be used for more than 2 classes?


No, logistic regression is a binary classifier by default, so it cannot be applied to more than 2 classes. However, it can be extended to solve multi-class classification problems (multinomial logistic regression)
 

15. Explain correlation and covariance?


Correlation is used to measure and estimate the quantitative connection between two variables. Correlation estimates how strongly two variables are associated. Examples like income and expense, demand and store, etc.
Covariance is a straightforward way to calculate the correlation between two variables. The problem with covariance is that it is hard to compare them without normalization.
 

16. What is P-value?


P-values ??are used to make hypothesis test decisions. The p-value is the minimum influential level at which you can refuse the null hypothesis. The minimum the p-value, the more likely you will leave the null hypothesis.
 

17. What are parametric and non-parametric models?


Parametric models will have limited parameters; you only need to know the model parameter to predict new data.
Non-parametric models have no restrictions on accepting multiple parameters, allowing for greater flexibility and prediction of new data. You need to know the condition of the data and model parameters.
 

18. How to handle outliers?


An outlier is an observation in a data set far from the other observations in the data set. The tools used to detect outliers are
Box plot, Z-score, Scatter plot, etc.
We usually need to follow three simple strategies to deal with outliers:
We can drop them off.
We can keep them as outliers and retain them as a feature.
Similarly, we can transform quality to reduce the effect of outliers.
 

19. What is reinforcement learning?


Reinforcement learning differs from other types of learning, such as supervised and unsupervised learning. In reinforcement learning, we are not given data or labels. Our learning is based on the rewards provided to the agent by the environment.
 

20. Difference between Sigmoid and Softmax functions?


A sigmoid function is operated for binary classification. The Sum of probabilities must be 1. While the Softmax function is used for multiple classifications. The Sum of the possibilities will be 1.
 

Conclusion


Machine learning is progressing so fast; therefore, new concepts emerge.
In this blog, we have seen 20 most frequently asked questions about machine learning and their relevant answers for interviewing freshers. We wish this blog has helped you on your journey to becoming a machine learning engineer and related work.