Skip to content
Machine Learning
All coursesPlacement Report
‹ Back to previous sectionCourse homeAll page URLs

ML Fundamentals Interview Questions for Machine Learning

25 ML Fundamentals questions with answers for machine learning roles — 10 easy, 12 medium, 3 hard.

About ML Fundamentals

Core ideas every ML interview starts with: types of learning, regression versus classification, bias and variance, overfitting, train-validation-test splits, loss functions and gradient descent. These questions test whether you can frame a business problem as an ML problem, build a sensible baseline and reason about why a model generalises or fails on unseen data.

Sheet progressStart practicing
3 QUESTIONS FREESign up to unlock the full question bank and track your progress.Unlock every question and answer, then continue practising company-wise and package-wise.
3 questions free Unlock the rest in seconds
01

What is machine learning and how is it different from traditional programming?

ML FundamentalsEasy
Save to ▾
Show answer
Machine learning is a way of building programs that learn patterns from data instead of following hand-written rules. In traditional programming you give rules and data to get answers; in ML you give data and answers to get the rules, stored as model parameters. For example, a spam filter learns which word patterns indicate spam from labelled emails rather than from a fixed keyword list.
02

Explain supervised, unsupervised and reinforcement learning with one example each.

ML FundamentalsEasy
Save to ▾
Show answer
Supervised learning trains on labelled data, such as predicting house prices from past sales. Unsupervised learning finds structure in unlabelled data, such as grouping customers into segments with k-means. Reinforcement learning trains an agent through rewards and penalties from an environment, such as a program learning to play a game. Semi-supervised and self-supervised learning sit between these when labels are scarce.
03

What is the difference between a regression and a classification problem?

ML FundamentalsEasy
Save to ▾
Show answer
Regression predicts a continuous number, such as sales next month or delivery time in minutes. Classification predicts a discrete category, such as fraud or not fraud, or one of five product types. The choice decides the loss function and metrics: regression uses MSE, MAE or R-squared, while classification uses log loss, accuracy, precision, recall or AUC.
04

What are overfitting and underfitting, and how do you detect them?

ML FundamentalsEasy
Save to ▾
Show answer
Overfitting is when a model memorises training noise and performs much worse on unseen data. Underfitting is when the model is too simple to capture the pattern, so both training and validation scores are poor. Detect them by comparing training and validation metrics or plotting learning curves. A large gap means overfitting; two low, close scores mean underfitting.
05

Why do we split data into training, validation and test sets?

ML FundamentalsEasy
Save to ▾
Show answer
The training set fits model parameters, the validation set is used to choose hyperparameters and compare models, and the test set gives a final unbiased estimate of performance. If you tune on the test set, its score becomes optimistic because information has leaked into model choices. A common split is 70/15/15, or train/test plus cross-validation on the training part.
06

What is the difference between parameters and hyperparameters?

ML FundamentalsEasy
Save to ▾
Show answer
Parameters are learned from data during training, such as the weights of a linear regression or split thresholds of a tree. Hyperparameters are set before training and control the learning process, such as learning rate, max_depth, number of trees or regularisation strength C. Hyperparameters are tuned with validation data using grid search, random search or Bayesian optimisation.
07

How do you set up a basic train-test split with scikit-learn?

ML FundamentalsEasy
Save to ▾
Show answer
Use train_test_split with a fixed random_state for reproducibility and stratify for classification so class ratios match. from sklearn.model_selection import train_test_split X_train, X_test, y_train, y_test = train_test_split( X, y, test_size=0.2, random_state=42, stratify=y ) For time series, avoid random splits and use the most recent period as the test set.
08

Write a minimal scikit-learn workflow to train and evaluate a model.

ML FundamentalsEasy
Save to ▾
Show answer
The fit, predict, score pattern is the same for almost every estimator. from sklearn.linear_model import LogisticRegression from sklearn.metrics import accuracy_score model = LogisticRegression(max_iter=1000) model.fit(X_train, y_train) preds = model.predict(X_test) print(accuracy_score(y_test, preds)) Use predict_proba when you need probabilities for thresholds or ranking.
09

How would you build a quick baseline before trying complex models?

ML FundamentalsEasy
Save to ▾
Show answer
Use a DummyClassifier or DummyRegressor to see the score of a trivial guess, then a simple model. from sklearn.dummy import DummyClassifier base = DummyClassifier(strategy='most_frequent') base.fit(X_train, y_train) print(base.score(X_test, y_test)) Any real model must clearly beat this number; otherwise the features carry little signal.
10

Why do you want to work as a machine learning engineer?

ML FundamentalsEasy
Save to ▾
Show answer
I enjoy turning data into decisions that people actually use. During my projects I liked the full cycle: framing the problem, cleaning messy data, comparing models and explaining results. ML engineering lets me combine coding with statistics and see measurable business impact, such as reducing manual review time. I also want to keep growing into deployment and monitoring, which I am already practising with FastAPI and Docker.
11

What is the bias-variance trade-off?

ML FundamentalsMedium
Save to ▾
Show answer
Bias is error from overly simple assumptions, which causes underfitting. Variance is error from sensitivity to the particular training sample, which causes overfitting. Increasing model complexity usually lowers bias but raises variance. The goal is the complexity that minimises total test error. For example, a linear model on curved data has high bias, while a very deep unpruned tree has high variance.
12

What is a loss function? Name common ones for regression and classification.

ML FundamentalsMedium
Save to ▾
Show answer
A loss function measures how wrong a prediction is for one example, and training minimises its average over the data. Regression commonly uses mean squared error, mean absolute error or Huber loss, which is robust to outliers. Classification uses log loss, also called cross-entropy, and hinge loss for SVMs. The choice should reflect the business cost of errors.
13

Explain gradient descent and the role of the learning rate.

ML FundamentalsMedium
Save to ▾
Show answer
Gradient descent minimises a loss by repeatedly moving parameters in the direction of the negative gradient: w = w - learning_rate * gradient. The learning rate controls step size. Too high and the loss oscillates or diverges; too low and training is very slow or gets stuck. Variants include batch, stochastic and mini-batch gradient descent, plus adaptive optimisers like Adam.
14

What is the difference between batch, stochastic and mini-batch gradient descent?

ML FundamentalsMedium
Save to ▾
Show answer
Batch gradient descent uses the full dataset for each update, giving stable but slow and memory-heavy steps. Stochastic gradient descent updates after every single example, which is fast and noisy and can escape shallow minima. Mini-batch uses small groups such as 32 to 512 rows, balancing stability with speed and allowing vectorised computation. Mini-batch is the default in practice.
15

What is the curse of dimensionality?

ML FundamentalsMedium
Save to ▾
Show answer
As the number of features grows, data becomes sparse in the feature space, distances between points become similar, and the amount of data needed to generalise grows rapidly. Distance-based methods like k-NN and k-means suffer most. Remedies include feature selection, dimensionality reduction such as PCA, regularisation and collecting more data. Text and genomics data often face this problem.
16

Your model shows 99 percent training accuracy but 72 percent validation accuracy. What will you do?

ML FundamentalsMedium
Save to ▾
Show answer
This is overfitting. I would first check for leakage or a split mismatch, then simplify the model: add regularisation, reduce depth or features, use early stopping, or prune trees. I would gather more data or use augmentation if possible and use cross-validation to confirm the fix. Plotting learning curves shows whether more data would actually help.
17

A business stakeholder asks you to use deep learning for a 5,000-row tabular dataset. How do you respond?

ML FundamentalsMedium
Save to ▾
Show answer
I would explain that for small tabular data, gradient-boosted trees or regularised linear models usually match or beat neural networks, train faster and are easier to explain. I would propose building a simple baseline, then a boosting model, and only test a neural network if there is a clear reason. I would show the comparison on cross-validated metrics so the decision is data-driven.
18

You are asked to predict customer churn. How would you frame the ML problem from scratch?

ML FundamentalsMedium
Save to ▾
Show answer
First define churn precisely, for example no purchase in 60 days, and the prediction window. It becomes binary classification. Build features from data available before the cut-off date only, choose a time-based split, and pick metrics like recall at a fixed precision or lift in the top decile. Start with logistic regression as a baseline, then try boosting, and agree on how retention teams will use the scores.
19

Leadership wants a model in one week, but data quality is poor. How do you handle it?

ML FundamentalsMedium
Save to ▾
Show answer
I would agree on a narrow first version: a simple, explainable baseline built on the cleanest available fields, with clear notes on data gaps and expected accuracy. In parallel I would log specific data issues and their impact. Delivering a working baseline with an honest limitations section builds trust and gives a reference point for improvement in later iterations.
20

How do you implement simple gradient descent for linear regression in NumPy?

ML FundamentalsMedium
Save to ▾
Show answer
Compute predictions, the gradient of MSE, and update weights in a loop. import numpy as np w = np.zeros(X.shape[1]); b = 0.0; lr = 0.01 for _ in range(1000): err = X @ w + b - y w -= lr * (2 / len(y)) * (X.T @ err) b -= lr * (2 / len(y)) * err.sum() Scale features first or the loop converges slowly.
21

How do you make an ML experiment reproducible?

ML FundamentalsMedium
Save to ▾
Show answer
Fix random seeds in NumPy, Python and the model, pin library versions in requirements.txt, version the data or record its hash, and save the exact code commit and hyperparameters. Tools like MLflow log parameters, metrics and artifacts per run. Use pipelines so preprocessing is identical each time, and keep the train-test split stored rather than recreated randomly.
22

Tell me about a time your model did not work as expected. What did you do?

ML FundamentalsMedium
Save to ▾
Show answer
Situation: my first churn model scored 0.95 AUC in validation but failed in a pilot. Task: find out why. Action: I traced features and found one field updated after customers left, which leaked the label. I removed it, rebuilt features with a strict cut-off date and moved to a time-based split. Result: AUC dropped to 0.78 but held steady in production, and I added a leakage checklist to my workflow.
23

What is the no free lunch theorem and what does it mean in practice?

ML FundamentalsHard
Save to ▾
Show answer
It states that no single algorithm is best for every problem when averaged over all possible problems. In practice it means you should try several model families, start with a simple baseline, and choose based on validation results for your data. For tabular data gradient boosting often wins, but a logistic regression may be preferred if interpretability matters more.
24

What is the difference between a generative and a discriminative model?

ML FundamentalsHard
Save to ▾
Show answer
A discriminative model learns the decision boundary or P(y|x) directly, such as logistic regression or SVM. A generative model learns how data is produced, P(x|y) and P(y), and can generate new samples, such as Naive Bayes or Gaussian mixture models. Discriminative models usually give better accuracy with enough data, while generative models handle missing data and small samples better.
25

Your training loss is not decreasing at all. What could be wrong?

ML FundamentalsHard
Save to ▾
Show answer
Common causes are a learning rate that is too high or too low, unscaled features, a bug in labels or feature alignment, wrong loss for the task, or data that has been shuffled away from its labels. I would try overfitting a tiny subset first; if the model cannot even fit 50 rows, there is a bug. Then check scaling, learning rate and gradient values.

Frequently asked questions

How important is ML Fundamentals in Machine Learning interviews?

Core ideas every ML interview starts with: types of learning, regression versus classification, bias and variance, overfitting, train-validation-test splits, loss functions and gradient descent. These questions test whether you can frame a business problem as an ML problem, build a sensible baseline and reason about why a model generalises or fails on unseen data.

How should I use the ML Fundamentals sheet?

Go top to bottom — easy to hard. Answer before opening each answer, tick what you solved and save misses to Last-Minute Revision.

More question sheets

Data Preprocessing and Feature Engineering questionsRegression Models questionsClassification Models questionsDecision Trees and Ensembles questionsClustering and Dimensionality Reduction questionsModel Evaluation and Tuning questionsMLOps and Deployment questions