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Machine learning is making this world smarter and more efficient with every passing day. It is a life-changing technology that is rapidly transforming our way of living.
Being used globally as one of the essential elements for a smart ecosystem, there are numerous machine learning job opportunities available for eligible professionals.
If desired, you can pursue online or offline machine learning courses and get certified to step in as a machine learning engineer or similar role in the IT sector.
A stack of required education degrees and certifications along with an impeccable machine learning resume will undoubtedly take you closer to your targeted job.
However, you need to crack the interview round impressively to get selected in this highly competitive environment.
Here we are elaborating on the process of reaching the interview process along with some machine learning interview questions and answers to help you in getting prepared to crack it.
Steps Before Reaching the Interview Process
Just like any other job application, you will have to follow the traditional application procedure to get a machine learning job. Check out the below-given steps that can help you get closer to the interview round for a machine learning job.
- Get an B.S. degree in computer science or certifications related to machine learning as you have to be with a technical background to join as a machine learning engineer.
- Craft an ATS friendly professional machine learning resume for the application process according to the job description
- Make a cover letter for your job application as most of the recruiters take it as an important element in the recruitment process.
- Apply by providing genuine details and send your application equipped with your machine learning resume and cover letter
- At first, your resume might face a virtual screening round via the Applicant Tracking System that will filter out irrelevant resumes
- Secondly, a recruiter will screen your job application and attached documents and you might receive call for a general introduction round.
- Lastly, you will face the final step i.e. interview round where you will have to answer all the machine learning questions asked appropriately.
Resume building is also one of the most important steps to grab your desired job. Hence, you need an expert to help you in crafting an impressive resume.
You can use Hiration’s Online Resume Builder for this task. It is powered by Artificial Intelligence technology that can help you in framing your resume appropriately.
Also Read- Machine Learning Resume
Machine Learning Interview QuestionsWe have listed a few important machine learning interview questions and answers for you. These machine learning questions will give you an overview of the topics that a recruiter might ask during the interview.
1. Explain Supervised and Unsupervised Learning.
The machine learning algorithm used for inferring any specific function from labeled training data can be termed Supervised Learning. Considering unsupervised learning, it is the machine learning algorithm that is used to find patterns on the given data set. Some examples of supervised and unsupervised learning algorithms are:
|Supervised Learning Algorithms||Unsupervised Learning Algorithms|
|Support Vector Machines||Clustering|
|Naive Bayes||Neural Networks|
|Decision Trees||Latent Variable Models|
2. What is SVM Algorithm?
SVM can be termed as a Support Vector Machine. It is a strong and effective machine learning model that is used to perform regression, linear or non-linear classification, and outlier detection.
3. How many types of Kernels are there in SVM?
There are 4 types of Kernels available in a Support Vector Machine:
- Polynomial Kernel- It is used for discrete data without any notion of smoothness
- Sigmoid Kernel- It is used for neural networks as an activation function
- Linear Kernel- It is used for linearly separable data
- Radial Basis Kernel- It is used to build a decision boundary
4. What do you mean by cross-validation?
Cross-validation is a process that allows you to split your data into three parts:
- Training Data
- Testing Data
- Validation Data
This process splits your data into K-subsets and the model starts getting trained from K-1 of those subsets.
Testing is done in the last subset and this process is compulsory for every subset. Ultimately, the final score is produced as the average score of all the K-folds.
5. What is the difference between Regression and Classification?
There are 2 major differences between regression and classification:
|It is used to get discrete results||It deals with continuous data|
|It is used to predict output||It predicts the relationship of data sets|
6. What do you mean by Neural Network?
As its name suggests, Neural Network is a simplified network of the human brain. It is an artificial machine network, however, it works the same way as the brain. It has neurons that are used to pass information as they get activated with every motion or activity.
7. What is Collaborative & Content-Based Filtering?
Collaborative filtering is a specific technique used to get personalized content recommendations. It predicts content as per your interest and requirement along with the user preference for personalized results.
On the other hand, content-based filtering only focuses on user preference based on previous choices and provides results without considering your interest.
8. What do you mean by Clustering?
The process of grouping similar data or points into different groups according to their types can be termed as clustering. Some examples of majorly used clustering techniques are:
- K means Clustering
- Hierarchical Clustering
- Fuzzy Clustering
- Density-based Clustering etc.
9. Differentiate between Deductive and Inductive Learning?
The inductive learning model uses examples of observed instances to learn and draw generalized conclusions. On the other hand, the deductive learning model implies the conclusion before drawing it.
|Inductive Learning Model||Deductive Learning Model|
|It uses observations for drawing conclusions||It uses conclusions for making observations|
10. Explain the difference between Data Mining & Machine Learning.
Data mining is the process of abstracting knowledge via structured data with the help of machine learning algorithms.
Machine learning is the process of studying, designing, and developing algorithms that allow the processors to learn without any command or guidance.
11. What makes Machine Learning different from Deep Learning?
Machine Learning includes algorithms to parse data and learn from it to make informed decisions for any given task.
Deep learning can be termed as a subset of machine learning that works on the principles of the human brain and is most useful in feature detection.
12. How many kinds of algorithm methods are used in Machine Learning?
There are majorly 5 types of algorithms used in machine learning. They are:
- Reinforcement Learning
- Supervised Learning
- Semi-Supervised Learning
- Unsupervised Learning
13. What do you mean by model selection in Machine Learning?
Model selection in machine learning can be defined as the selection of the appropriate from a stack of different mathematical models to define a particular dataset. Model learning plays a vital role in data mining, machine learning, and statistics.
14. Explain the three stages of model building in Machine Learning.
The three stages of model building in machine learning are:
Model Building: It selects a suitable algorithm that can train the model as per the requirement.
Model Application: This part analyzes the model to address the accuracy standards via test data.
Model Testing: It is the final part where the required changes after testing get implemented to apply the final model
15. Explain ILP.
ILP (Inductive Logic Programming) can be termed as a branch of machine learning that deals with logic programming. It is responsible to search and choose the patterns in data that help in building predictive models. The logic programs in this process are considered as hypotheses.
Also Read: How to Become a Machine Learning Engineer?
16. What are the functions of Supervised & Unsupervised Learning?
The major functions of supervised and unsupervised learning are:
|Supervised Learning||Unsupervised Learning|
|Speech Recognition||Find low-dimensional data representations|
|Predict Time Series||Find novel observations|
|Classification||Find data clusters|
|Annotate Strings||Find correlations and coordinates|
|Regression||Find interesting data directions|
17. What do you know about classifiers in Machine Learning?
A classifier can be defined as a case of discrete-valued function or hypothesis that can be used for class label assignments for selected data points. It inputs the vector of continuous or discrete feature values to get a single discrete value as the final result.
18. Differentiate between Machine Learning & Statistical Modeling.
Machine learning is used to make precise predictions related to scenarios such as stock price, footfall in a restaurant, etc. Whereas statistical modeling is used to determine the reason for interference between the variables such as the reason that increase or decrease the footfall in the restaurant.
19. What makes discriminative and generative models different from each other?
A generative model learns from the available categories of data, whereas a discriminative model only grabs the distinctions between available data sets. The discrimination model performs much better than the generative model in terms of classification tasks
20. What is a hash table?
Hashing is used to identify desired unique objects from a stack of similar objects. Hash functions are all the large elements that are converted into small keys i.e hash functions via hashing techniques. The values of all the hash functions are stored in special data structures known as hash tables.
21. What is the default process of decision tree splitting?
The generic process to split a decision tree can be termed as the Gini Index (i.e. the impurity measure of a selected node). You can change it by rectifying the classifier parameters.
22. Can you use logistic regression for classes more than 2?
No, you can not use logistic regression for more than 2 classes because it is a binary classifier. Naive Bayes Classifiers will be the perfect option for multi-class classification algorithms such as Decision Trees.
23. Showcase the distance metrics that can be used in KNN.
The distance metrics that can be used in KNN are:
24. What measures can be used to rectify the class imbalance in any classification issue?
You can deal with the class imbalance via certain measures such as:
- By using sampling
- Via class weights
- By choosing loss functions
- Via SMOTE
25. Showcase the benefits of pruning?
Pruning can benefit you in many ways, such as:
- It minimizes the tree size
- It increases bias
- It reduces overfitting
- It reduces model complexity
26. Explain the machine learning model building process?
You need to follow certain steps to build a model in machine learning such as:
- Understand and analyze the entire business model appropriately
- Perform data acquisition task
- Perform data cleaning task
- Perform exploratory data analysis task
- Make a model with the help of machine learning algorithms
- Check the accuracy of your model with the help of an unknown dataset
27. What is Underfitting?
The issue emerging from the low-level error in the testing and training sets can be termed as underfitting. Some algorithms in machine learning perform well for interpretations but are not effective for predictions.
28. What are the two essential components of a Bayesian logic program?
There are 2 essential components of a Bayesian logic program:
- Logical Components
- Quantitative Components
29. What are the measures of checking the normality of a dataset?
You can use plots for visual operation. Some normality checks that you can consider are:
- Anderson-Darling Test
- Kolmogorov-Smirnov Test
- Shapiro-Wilk W Test
- D’Agostino Skewness Test
- Martinez-Iglewicz Test
30. How can you discover the Outlier Values?
To discover outliers, you need to use tools such as:
- Scatter plot
- Box plot, etc
31. What is Boosting & Bagging?
Bagging is a part of assemble learning that helps in improving unstable classification or estimation schemes. Whereas, Boosting is used to reduce the bias of the entire combined model.
32. What is dimension reduction in machine learning.
As its name suggests, dimension reduction is used to reduce the random variable numbers under specific considerations. Dimension reduction is further divided into two parts, i.e., feature selection & extraction.
33. What is Cluster Sampling?
Cluster Sampling is a method of selecting groups within a pre-defined population, sharing similar characteristics. A Cluster Sample can be a collection or cluster of elements in itself.
34. What is F1 score?
The F1 score determines model's performance. A model scoring 1 is considered as best and the models with 0 are the worst. You can use this model in classification tests where true negatives do not matter.
35. What is lazy learning algorithm and what is its relation with instance-based learning algorithm?
Lazy leraning can be defined as a process where generalization and iduction methods are delayed intly you perform classification. The instance-based learning algorith possess same property. Hence it is also called lazy learning algorithm sometimes.
Hiration Job Assistance
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Apart from this, you can also check out other features and products of Hiration tthat can help you prove your worth to the recruiters during the recruitment process:
All of these above-given machine learning interview questions have been taken out keeping the current level of competition. You can use these deep learning interview questions to prepare for your upcoming machine learning-focused interview round.
You can also leverage the other products of Hiration to perform well before and after the machine learning job application process. Still, if you have any confusion, always feel free to contact our experts at firstname.lastname@example.org. Our experts will try to resolve your issue as soon as possible.