Machine Learning implies the procedure in which a computer program is trained to build a statistical model based on the given data. The purpose of the procedure (ML) is to identify the key patterns out of data or to get key insights.
The reason for the tremendous growth of Machine Learning trend is because Machine Learning solves Real-World problems. Machine learning algorithms learn from the data and solve complex problems within a glimpse. Undoubtedly, it pays off for early adopters.
In this blog, we will discuss the top AI and ML interview questions asked in the reputed institutions like Microsoft, Amazon, etc.
Artificial Intelligence and Machine Learning Interview Questions 2022
Some of the top questions that are often asked in AIML interviews are:
Artificial Intelligence and Machine Learning Interview Questions for Freshers:
Q1. What do you mean by Artificial Intelligence?
Ans: The field of computer science technology which emphasizes creating intelligent machines capable of mimicking human behavior.
Q2. What are the different domains/Subsets of AI?
Ans: Machine Learning, Deep Learning, Neural Network, Expert System, Fuzzy Logic, Natural Language Processing, Robotics, Speech Recognition.
Q3. What are the types of AI?
Ans: 1. Based on Capabilities:
Weak AI or Narrow AI
2. Based on Functionalities:
Theory of Mind
Q4. Why do we need Artificial Intelligence?
Ans: To create intelligent machines capable of mimicking human behavior, to solve complex problems, in brief to save manpower.
Q5. What is Deep Learning?
Ans: A subset of Machine learning purposed by the human brain cellsto mimic the working of the human brain
Q6. State some real-world applications of deep learning.
Adding colors to the b/w images
Deep-Learning Robots, etc.
Q7. What is the intelligent agent in AI, and where are they used?
Ans: An autonomous entity which perceives its surroundings through the sensors and acts on it to achieve the goal using actuators.
Q8. Where are intelligent agents in AI used?
Ans: The Intelligent agents in AI are used in Information Access and Navigations such as Search Engine, Repetitive Activities, Domain Experts, Chatbots, etc.
Q9. Why was Machine Learning Introduced?
Ans: In simple words, to simplify our living style in a fast, competitive world.
Q10. What are Different Types of Machine Learning algorithms?
• Supervised (Classification, Regression, Ranking)
• Unsupervised (Clustering, Segmentation, Associate Mining, Dimension Reduction)
• Reinforcement (Reward Systems, Decision Process, Recommendation Systems)
Q11. What is Supervised Learning?
Ans: The machine learning algorithm which infers a function from labeled training data is called supervised learning.
Q12. What is Unsupervised Learning?
Ans: The machine learning algorithm used to find patterns on the set of data given without any dependent variable is unsupervised learning.
Q13. What is ‘Naive’ in a Naive Bayes?
Ans: A supervised machine learning algorithm which applies Bayes Theorem to make assumptions is known as Naïve Bayes method.
Q14. What is PCA? When do you use it?
Ans: Principal component analysis is a process used for dimension reduction by measuring the variation in each variable which makes the dataset easier to visualize.
Q15. Explain SVM Algorithm in Detail.
Ans: In the SVM algorithm, we try to see whether a data point considered as a p-dimensional vector (a list of p numbers) is separable with a (p-1)-dimensional hyperplane.
Q17. What are Support Vectors in SVM?
Ans: In SVM, the points of the edge of the dividing hyperplane between the different classes which maximize the distance from the line to the points of the classes are called Support Vectors.
Q18. What are Different Kernels in SVM?
• Linear kernel
• Polynomial kernel
• Radial basis kernel
• Sigmoid kernel
Q19. State some real-world applications of AI.
Ans: Google Search Engine, Ridesharing Applications, Spam Filters in Email, Social Networking, Product recommendations.
Q20. How is machine learning related to AI?
Ans: Machine learning is a subset of Artificial intelligence. AI uses various types of Machine learning algorithms to solve complex problems.
Q21. What are the different elements of MDP?
Ans: MDP has four elements:
A set of finite states S
A set of finite actions A
Q22. What do you understand by the hyperparameter?
Ans. The parameters that determine and control the complete training process.
Q23. Explain the term Q-Learning.
Ans: A popular algorithm based on the Bellman equation used in reinforcement learning. Q represents the quality of the actions in each state, and the goal of the agent is to maximize Q.
Q24. Which programming languages are used for AI?
Ans: Python, Java, Lisp, R, Prolog.
Q25. What is Cross-Validation?
Ans: A method of splitting the entire data into training, testing, and validation data which is split into k subsets, the model being trained on (k-1) of those datasets.
Artificial Intelligence and Machine Learning Interview Questions for Experienced
Q1. What is F1 score? How would you use it?
Ans: A measure of the model’s accuracy, weighted average of recall and precision scores:
F1 = 2TP/2TP + FP + FN
Q2. Define Precision.
Ans: Precision helps in finding out truly relevant items amongst all predicted by the classifier.
Q3. How to Tackle Overfitting and Underfitting?
Ans: In Overfitting we need to edit the data and estimate the model accuracy with, say k-fold cross-validation. In case of Underfitting we need to redo the algorithms or add more data points.
Q4. What is a Neural Network?
Ans: A simplified version of the human brain with neurons which on encountering something similar gets activated.
Q5. What are Loss Function and Cost Functions?
Ans: Loss function is to consider only a single data point when calculating loss.
Cost function is used for multiple data points.
Q6. Explain the key Difference Between Loss Function and Cost Functions.
Ans: Loss function is to calculate the difference between the actual and predicted values for a single data point while cost functions are for the entire training dataset.
Q7. What is Ensemble learning?
Ans: A combined method of several machine learning models to produce more sophisticated models.
Q8. How do you make sure which Machine Learning Algorithm to use?
Ans: It all depends on the exploratory data analysis EDA, no other specific way.
Q9. How to Handle Outlier Values?
Ans: Drop them or mark and include the outliers as a feature. Or transform the feature to reduce the impact of the outlier.
Q10. What is a Random Forest?
Ans: A versatile machine learning method capable of performing both classification and regression tasks.
Q11. How does Random Forest work?
Ans: Take a sample size from the training data and then start with a single node.
If the node size is bigger than the number of observations then stop.
Select random variables.
Search the variable with the best job for splitting the observations.
Split the observations into two.
Restart the process on each of them.
Q12. What is Recall?
Ans: Recall helps in finding out the no. of items found by the classifier from the truly relevant ones.
Q13. What is Collaborative Filtering?
Ans: A proven technique to predict new content by matching the preferences of many users with the interests of the individual user.
Q14. Content-Based Filtering?
Ans: Content-based recommender systems are focused on making new recommendations from similar content as per the user’s previous choices.
Q15. What is Clustering?
Ans: Process of grouping a set of objects into a number of groups.
Q16. How can you select K for K-means Clustering?
Ans: Direct methods: Elbow and silhouette.
Or Statistical testing methods: Gap statistics.
Q17. What are Recommender Systems?
Ans: A system to predict and recommend products and users’ interests.
Q18. How to check the Normality of a dataset?
D’Agostino Skewness Test
Q19. Can logistic regression be used for more than 2 classes?
Q20. Explain Correlation and Covariance?
Ans: Correlation measures the strength of relation of two variables.
Covariance is a process to calculate the correlation between two variables.
Q21. What is P-value?
Ans: The minimum significant level where one can reject the null hypothesis, used to make a decision about a hypothesis test.
Q22. What are Parametric and Non-Parametric Models?
Ans: Parametric models have limited parameters. Non-Parametric models have an unlimited number of parameters, hence more flexibility in predicting new data.
Q23. What is Reinforcement Learning?
Ans: Learning based on the rewards given to the agent by the surroundings.
Q24. Difference Between Sigmoid and Softmax functions?
Ans: The sigmoid function is used for binary classification. While the softmax function is used for multi-classification. The probabilities sum should be 1 in both.
Q25. State the types of Clustering.
K means clustering
Fuzzy clustering, etc.
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