2. What are the different types of Learning/ Training models in ML?

3. What is the difference between deep learning and machine learning?

4. What is the main key difference between supervised and unsupervised machine learning?

5. How do you select important variables while working on a data set? 

6. There are many machine learning algorithms till now. If given a data set, how can one determine which algorithm to be used for that?

7. How are covariance and correlation different from one another?

8. State the differences between causality and correlation?

9. We look at machine learning software almost all the time. How do we apply Machine Learning to Hardware?

10. Explain One-hot encoding and Label Encoding. How do they affect the dimensionality of the given dataset?

11. When does regularization come into play in Machine Learning?

12. What is Bias, Variance and what do you mean by Bias-Variance Tradeoff?

13. How can we relate standard deviation and variance?

14. A data set is given to you and it has missing values which spread along 1 standard deviation from the mean. How much of the data would remain untouched?

15. Is a high variance in data good or bad?

16. If your dataset is suffering from high variance, how would you handle it?

17. A data set is given to you about utilities fraud detection. You have built aclassifier model and achieved a performance score of 98.5%. Is this a goodmodel? If yes, justify. If not, what can you do about it?

18. Explain the handling of missing or corrupted values in the given dataset.

19. What is Time series?

20. What is a Box-Cox transformation?

21. What is the difference between stochastic gradient descent (SGD) and gradient descent (GD)?

22. What is the exploding gradient problem while using the back propagation technique?

23. Can you mention some advantages and disadvantages of decision trees?

24. Explain the differences between Random Forest and Gradient Boosting machines.

25. What is a confusion matrix and why do you need it?

26. What’s a Fourier transform?

27. What do you mean by Associative Rule Mining (ARM)?

28. What is Marginalisation? Explain the process.

29. Explain the phrase “Curse of Dimensionality”.

30. What is the Principle Component Analysis?

31. Why is rotation of components so important in Principle Component Analysis (PCA)?

32. What are outliers? Mention three methods to deal with outliers.

33. What is the difference between regularization and normalisation? 

34. Explain the difference between Normalization and Standardization.

35. List the most popular distribution curves along with scenarios where you will use them in an algorithm.

36. How do we check the normality of a data set or a feature? 

37. What is Linear Regression?

38. Differentiate between regression and classification.

39. What is target imbalance? How do we fix it? A scenario where you have performed target imbalance on data. Which metrics and algorithms do you find suitable to input this data onto? 

40. List all assumptions for data to be met before starting with linear regression.

41. When does the linear regression line stop rotating or finds an optimal spot where it is fitted on data? 

42. Why is logistic regression a type of classification technique and not a regression? Name the function it is derived from? 

43. What could be the issue when the beta value for a certain variable varies way too much in each subset when regression is run on different subsets of the given dataset?

44. What does the term Variance Inflation Factor mean?

45. Which machine learning algorithm is known as the lazy learner, and why is it called so?

46. Is it possible to use KNN for image processing? 

47. Differentiate between K-Means and KNN algorithms?

48. How does the SVM algorithm deal with self-learning? 

49. What are Kernels in SVM? List popular kernels used in SVM along with a scenario of their applications.

50. What is Kernel Trick in an SVM Algorithm?

51. What are ensemble models? Explain how ensemble techniques yield better learning as compared to traditional classification ML algorithms. 

Machine Learning Interview Questions And Answers

1. Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning:

Ans.

• AI: Artificial Intelligence refers to the development of computer systems capable of performing tasks that typically require human intelligence. It encompasses a wide range of technologies and applications designed to mimic human intelligence.
• ML: Machine Learning is a subset of AI that involves the development of algorithms allowing systems to learn patterns and make decisions without explicit programming. It focuses on the development of models that can learn from data.
• Deep Learning: Deep Learning is a specific type of ML that uses neural networks with multiple layers (deep neural networks) to learn and make decisions. It is particularly effective in handling large volumes of data.

2.Types of Learning/Training Models in ML:

Ans.

• Supervised Learning: The model is trained on a labeled dataset, where the algorithm learns to map input data to the corresponding output.
• Unsupervised Learning: The model is given unlabeled data and must find patterns and relationships without explicit guidance.
• Reinforcement Learning: The model learns to make decisions by interacting with an environment and receiving feedback in the form of rewards or penalties.

3.Difference between Deep Learning and Machine Learning:

Ans.

• Machine Learning: Involves the development of models that can learn from data and make predictions or decisions.
• Deep Learning: A subset of ML that uses neural networks with multiple layers to learn complex patterns from data.

4.Key Difference between Supervised and Unsupervised Machine Learning:

Ans.

• Supervised Learning: Involves training a model on a labeled dataset with known outputs.
• Unsupervised Learning: Involves training a model on an unlabeled dataset, where the algorithm discovers patterns and relationships on its own.

5.Selecting Important Variables in a Dataset:

Ans.

Techniques include feature importance from tree-based models, correlation analysis, and recursive feature elimination.

6.Selecting Machine Learning Algorithm for a Dataset:

Ans.

• Depends on the problem type (classification, regression), dataset size, nature of data, and computational resources. Experimenting with multiple algorithms and assessing performance is often necessary.

7.Difference between Covariance and Correlation:

Ans.

• Covariance: Measures how two variables change together, but it doesn’t provide the strength or direction of the relationship.
• Correlation: Scales covariance by the standard deviations of the variables, providing a standardized measure with values between -1 and 1.

8.Differences between Causality and Correlation:

Ans.

• Causality: Implies a cause-and-effect relationship between variables.
• Correlation: Indicates a statistical association between variables but does not imply causation.

9.Applying Machine Learning to Hardware:

Ans.

• Involves developing ML models or algorithms tailored for hardware constraints, such as edge computing devices or IoT devices.

10.One-Hot Encoding and Label Encoding:

Ans.

• One-Hot Encoding: Converts categorical variables into binary vectors, increasing dimensionality.
• Label Encoding: Assigns a unique numerical label to each category, reducing dimensionality.

11.Role of Regularization in Machine Learning:

Ans.

• Regularization is applied to prevent overfitting in machine learning models. It adds a penalty term to the loss function, discouraging the model from fitting the noise in the training data.

12.Bias, Variance, and Bias-Variance Tradeoff:

Ans.

• Bias: Error due to overly simplistic assumptions in the learning algorithm.
• Variance: Error due to too much complexity in the learning algorithm.
• Bias-Variance Tradeoff: Balancing bias and variance to achieve optimal model performance.

13.Relating Standard Deviation and Variance:

Ans.

• Standard deviation is the square root of variance. Both measure the spread or dispersion of data.

14.Handling Missing Values Spread Along One Standard Deviation:

Ans.Approximately 68% of the data would remain untouched, as this range covers one standard deviation on either side of the mean in a normal distribution.

15.High Variance in Data:

Ans.High variance can lead to overfitting, where the model performs well on the training data but poorly on new data.

16.Handling High Variance in a Dataset:

Ans.Techniques include reducing model complexity, increasing training data, and using regularization methods.

Hridhya Manoj

Hello, I’m Hridhya Manoj. I’m passionate about technology and its ever-evolving landscape. With a deep love for writing and a curious mind, I enjoy translating complex concepts into understandable, engaging content. Let’s explore the world of tech together