DZone
Thanks for visiting DZone today,
Edit Profile
  • Manage Email Subscriptions
  • How to Post to DZone
  • Article Submission Guidelines
Sign Out View Profile
  • Post an Article
  • Manage My Drafts
Over 2 million developers have joined DZone.
Log In / Join
Refcards Trend Reports Events Over 2 million developers have joined DZone. Join Today! Thanks for visiting DZone today,
Edit Profile Manage Email Subscriptions Moderation Admin Console How to Post to DZone Article Submission Guidelines
View Profile
Sign Out
Refcards
Trend Reports
Events
Zones
Culture and Methodologies Agile Career Development Methodologies Team Management
Data Engineering AI/ML Big Data Data Databases IoT
Software Design and Architecture Cloud Architecture Containers Integration Microservices Performance Security
Coding Frameworks Java JavaScript Languages Tools
Testing, Deployment, and Maintenance Deployment DevOps and CI/CD Maintenance Monitoring and Observability Testing, Tools, and Frameworks
Partner Zones AWS Cloud
by AWS Developer Relations
Culture and Methodologies
Agile Career Development Methodologies Team Management
Data Engineering
AI/ML Big Data Data Databases IoT
Software Design and Architecture
Cloud Architecture Containers Integration Microservices Performance Security
Coding
Frameworks Java JavaScript Languages Tools
Testing, Deployment, and Maintenance
Deployment DevOps and CI/CD Maintenance Monitoring and Observability Testing, Tools, and Frameworks
Partner Zones
AWS Cloud
by AWS Developer Relations
Securing Your Software Supply Chain with JFrog and Azure
Register Today

Trending

  • 5 Key Concepts for MQTT Broker in Sparkplug Specification
  • Seven Steps To Deploy Kedro Pipelines on Amazon EMR
  • Replacing Apache Hive, Elasticsearch, and PostgreSQL With Apache Doris
  • Introduction To Git

Trending

  • 5 Key Concepts for MQTT Broker in Sparkplug Specification
  • Seven Steps To Deploy Kedro Pipelines on Amazon EMR
  • Replacing Apache Hive, Elasticsearch, and PostgreSQL With Apache Doris
  • Introduction To Git
  1. DZone
  2. Data Engineering
  3. AI/ML
  4. Top 10 Machine Learning Interview Questions 2019

Top 10 Machine Learning Interview Questions 2019

Check out ten machine learning interview questions and answers.

Vibhuthi Viswanathan user avatar by
Vibhuthi Viswanathan
CORE ·
Jul. 31, 19 · Opinion
Like (10)
Save
Tweet
Share
12.18K Views

Join the DZone community and get the full member experience.

Join For Free

Emerging technologies have taken the world by storm. The innovations, opportunities, and threats they have unleashed are like no other. Along with their growth, the demand for specialists in these areas has grown.

As per the findings of the latest industry report, jobs in emerging technologies like machine learning, artificial intelligence, and data science rank among the top emerging jobs. A career in emerging technologies such as machine learning, AI, or data science can be highly lucrative as well as intellectually stimulating.

In this article, I have compiled some of the most frequently asked machine learning interview questions with their corresponding answers. Machine learning aspirants, as well as experienced ML professionals, can use this to revise their fundamentals before the interview.

Machine Learning Interview Questions 2019

  1. Differentiate Machine Learning and Deep Learning

  2. Machine learning, a subset of artificial intelligence, provides the machines with the capability to learn and improve automatically without any explicit programming. Whereas Deep learning, a subset of machine learning, artificial neural networks that are capable of making intuitive decisions.

  3. What do you understand by the terms Recall and Precision?

  4. The recall is alternatively called a true positive rate. It refers to the number of positives that have been claimed by your model compared to the number of positives that are available throughout the data.

    Precision, which is alternatively called a positive predicted value, is based on prediction. It is a measurement of the number of accurate positives that the model has claimed as compared to the number of positives that the model has actually claimed.

  5. Differentiate between Supervised Machine Learning and Unsupervised Machine learning?

  6. In Supervised learning, the machine is trained with the help of labeled data, i.e., data that is tagged with the right answers. Whereas in unsupervised machine learning, the model learns by discovering information by itself. As compared to supervised learning models, unsupervised models are more preferred for performing difficult processing tasks.

  7. What is K-means and KNN

  8. K-means is an unsupervised algorithm that is used for the process of clustering problems and KNN or K nearest neighbors is a supervised algorithm that is used for the process of regression and classification.

  9. What makes Classification different from Regression

  10. Both these concepts are an important aspect of supervised machine learning techniques. With Classification, the output is classified into different categories for making predictions. Whereas Regression models are usually used to find out the relationship between forecasting and variables. A key difference between classification and regression is that in the former the output variable is discrete and it is continuous in the latter.

  11. How will you deal with missing data in a dataset?

  12. One of the greatest challenges faced by a data scientist pertains to the problem of missing data. You can attribute the missing values in many ways including assigning a unique category, row deletion, substituting with mean/median/mode, employing algorithms that support the support missing values, and forecasting the missing value to name a few.

  13. What do you understand by Inductive Logic Programming (ILP)?

  14. A subfield of machine learning, Inductive Logic Programming searches patterns in data by using logic programming to develop predictive models. This process assumes that logic programs are a hypothesis or background knowledge.

  15. What are the steps you need to ensure you don't overfit with a specific model?

  16. When the model is provided a large amount of data during training, it starts to learn from the noise and other wrong data from the data set. This makes it difficult for the model to learn to generalize new instances apart from the training set. There are three ways by which you can avoid overfitting in machine learning. The first way is by keeping the model simple, the second way is by using cross-validation techniques and thirdly, by using regularization techniques, for example, LASSO.

  17. What is Ensemble Learning?

  18. Ensemble methods are alternatively called learning multiple classifier systems or committee-based learning. Ensemble method refers to the learning algorithms that build classifier sets and then categorize new data points to make a choice of their forecasting. This method trains a number of hypotheses to address the same problem. The best example of ensemble modeling is the random forest trees where many decision trees are used for predicting the results.

  19. Name the steps that are required in a machine learning project?

  20. Some of the critical steps that you should take for achieving a good working model are collecting data, preparing data, selecting a machine learning model, model training, evaluating the model, tuning the parameter, and lastly, prediction.

Machine learning Data science neural network Interview (journalism)

Published at DZone with permission of Vibhuthi Viswanathan. See the original article here.

Opinions expressed by DZone contributors are their own.

Trending

  • 5 Key Concepts for MQTT Broker in Sparkplug Specification
  • Seven Steps To Deploy Kedro Pipelines on Amazon EMR
  • Replacing Apache Hive, Elasticsearch, and PostgreSQL With Apache Doris
  • Introduction To Git

Comments

Partner Resources

X

ABOUT US

  • About DZone
  • Send feedback
  • Careers
  • Sitemap

ADVERTISE

  • Advertise with DZone

CONTRIBUTE ON DZONE

  • Article Submission Guidelines
  • Become a Contributor
  • Visit the Writers' Zone

LEGAL

  • Terms of Service
  • Privacy Policy

CONTACT US

  • 600 Park Offices Drive
  • Suite 300
  • Durham, NC 27709
  • support@dzone.com

Let's be friends: