Top 10 Machine Learning Interview Questions & Answers

Machine Learning has evolved into the backbone of modern technology, propelling advances in artificial intelligence and data analysis. As the need for qualified individuals in this industry grows, it is critical to be well-prepared for interviews. We look at the top 10 machine learning interview questions and give detailed solutions to help you ace your next interview.

1. What is deep learning?

Deep learning is a subset of machine learning that involves neural networks with multiple layers (deep neural networks). These networks learn from data hierarchies and are capable of automatically learning representations of data.

2. Why does overfitting occur?

Overfitting occurs when a model learns the training data too well, including its noise and outliers. This can result in poor generalization to new, unseen data.

3. How do classification and regression differ?

Classification predicts a category or class, while regression predicts a numerical value.

4. What is supervised learning?

In supervised learning, the algorithm learns from labeled training data, making predictions or decisions without human intervention.

5. Explain SVM algorithm in detail.

Support Vector Machine (SVM) is a supervised machine learning algorithm used for classification and regression tasks. It works by finding the hyperplane that best divides a dataset into different classes.

6. Why is naive Bayes so naïve?

Naive Bayes is considered “naïve” because it assumes that the presence of a particular feature is independent of the presence of any other feature, which may not be true in real-world scenarios.

7. Define precision and recall.

Precision is the ratio of correctly predicted positive observations to the total predicted positives, while recall is the ratio of correctly predicted positive observations to the all observations in actual class.

8. Explain correlation and covariance.

Covariance measures the degree of joint variability of two random variables, while correlation is a standardized measure that indicates the strength and direction of the linear relationship between two variables.

9. How Do You Handle Missing or Corrupted Data in a Dataset?

Strategies include removing missing data, imputing missing values using statistical methods, or leveraging machine learning algorithms to predict missing values.

10. What is PCA? When do you use it?

Principal Component Analysis (PCA) is a dimensionality reduction technique. It is used to transform high-dimensional data into a lower-dimensional form while retaining the most important information.

In conclusion, mastering these machine learning training interview questions will not only enhance your technical prowess but also demonstrate your ability to think critically and apply your knowledge to real-world scenarios. Good luck!

For more information you can check this : Most Important Machine Learning Interview Questions