Basic Machine Learning Interview Questions: A Beginner’s Guide

Machine Learning (ML) is one of the most in-demand skills in today’s technology-driven world. Whether you are preparing for a data scientist, machine learning engineer, or AI researcher role, interviewers often start with basic ML questions to test your foundational knowledge. In this article, we’ll cover the most common basic machine learning interview questions to help you prepare effectively.

1. What is Machine Learning?

Machine Learning is a subset of Artificial Intelligence (AI) that enables systems to learn from data and improve performance without being explicitly programmed. It focuses on building algorithms that can detect patterns and make predictions or decisions.

2. What are the types of Machine Learning?

There are three main types:

Supervised Learning – Trains on labeled data (e.g., predicting house prices).
Unsupervised Learning – Works on unlabeled data to find hidden patterns (e.g., customer segmentation).
Reinforcement Learning – Learns by interacting with an environment and receiving rewards or penalties (e.g., game-playing AI).

3. What is Overfitting and Underfitting?

Overfitting happens when a model learns the training data too well, including noise, and performs poorly on new data.
Underfitting occurs when a model is too simple to capture the underlying patterns, resulting in low accuracy.

4. What is the difference between Classification and Regression?

Classification is predicting a category or class (e.g., spam or not spam).
Regression is predicting continuous values (e.g., predicting stock prices).

5. What is a Confusion Matrix?

A confusion matrix is a performance measurement tool for classification models. It shows True Positives, False Positives, True Negatives, and False Negatives to evaluate accuracy, precision, recall, and F1 score.

6. What is the difference between Supervised and Unsupervised Learning?

Supervised Learning uses labeled data to train models.
Unsupervised Learning uses unlabeled data and groups or structures it based on similarities.

7. What are Hyperparameters in Machine Learning?

Hyperparameters are external configurations set before training (e.g., learning rate, number of hidden layers in a neural network). Unlike parameters, they are not learned during training but defined by the data scientist.

8. What are some commonly used Machine Learning algorithms?

Linear Regression
Logistic Regression
Decision Trees
Random Forests
Support Vector Machines (SVM)
k-Nearest Neighbors (kNN)
Naïve Bayes
Neural Networks

9. What is Cross-Validation?

Cross-validation is a technique to evaluate the performance of a machine learning model by splitting the dataset into multiple folds. It helps reduce bias and ensures the model generalizes well to unseen data.

10. What are the key evaluation metrics in Machine Learning?

Accuracy – Percentage of correct predictions.
Precision – Ratio of true positives to all predicted positives.
Recall (Sensitivity) – Ratio of true positives to all actual positives.
F1 Score – Harmonic mean of precision and recall.
ROC-AUC – Measures the ability of a classifier to distinguish between classes.

Final Thoughts

These basic machine learning interview questions are often the first step in the hiring process. While they may seem simple, mastering these concepts builds the foundation for more advanced topics like deep learning, natural language processing, and reinforcement learning.

If you’re preparing for interviews, focus on both theory and practical implementation using Python libraries like Scikit-learn, TensorFlow, or PyTorch.