Top Machine Learning Interview Questions for Freshers in 2026

Top Machine Learning Interview Questions for Freshers in 2026

Breaking into the machine learning field as a fresher can be challenging, but being well-prepared for interviews can make all the difference. This comprehensive guide covers the most important ML interview questions you're likely to encounter in 2026, organized by category to help you prepare effectively.

Top Machine Learning Interview Questions for Freshers in 2026

Table of Contents

1. Fundamentals of Machine Learning

2. Supervised Learning

3. Unsupervised Learning

4. Model Evaluation and Optimization

5. Deep Learning Basics

6. Practical ML Questions

7. Coding and Implementation
   

1. Fundamentals of Machine Learning

What is Machine Learning?

Machine Learning is a subset of artificial intelligence that enables systems to learn and improve from experience without being explicitly programmed. It focuses on developing algorithms that can access data, learn from it, and make predictions or decisions based on patterns discovered in the data.

What are the main types of Machine Learning?

There are three primary types: supervised learning (learning from labeled data), unsupervised learning (finding patterns in unlabeled data), and reinforcement learning (learning through trial and error with rewards and penalties). Semi-supervised learning and self-supervised learning are also increasingly important approaches.

What is the difference between AI, ML, and Deep Learning?

Artificial Intelligence is the broadest concept of machines being able to carry out tasks in a smart way. Machine Learning is a subset of AI that focuses on the ability of machines to receive data and learn for themselves. Deep Learning is a subset of ML that uses neural networks with multiple layers to model complex patterns in data.

Explain overfitting and underfitting.

Overfitting occurs when a model learns the training data too well, including its noise and outliers, resulting in poor performance on new data. Underfitting happens when a model is too simple to capture the underlying patterns in the data, performing poorly on both training and test data. The goal is to find the right balance between model complexity and generalization ability.

What is the bias-variance tradeoff?

The bias-variance tradeoff is a fundamental concept in machine learning. Bias refers to errors from overly simplistic assumptions in the learning algorithm, leading to underfitting. Variance refers to errors from sensitivity to small fluctuations in the training set, leading to overfitting. The ideal model minimizes both bias and variance to achieve good generalization.

2. Supervised Learning

What is the difference between classification and regression?

Classification is used to predict discrete class labels or categories (like spam or not spam), while regression predicts continuous numerical values (like house prices or temperature). Both are supervised learning tasks, but they differ in the type of output they produce.

Explain the k-Nearest Neighbors (k-NN) algorithm.

k-NN is a simple, instance-based learning algorithm that classifies new data points based on their similarity to k nearest neighbors in the training data. It calculates distances (typically Euclidean) between the new point and existing points, then assigns the most common class among the k nearest neighbors. The choice of k is crucial for performance.

What is a Decision Tree and how does it work?

A Decision Tree is a flowchart-like structure where internal nodes represent features, branches represent decision rules, and leaf nodes represent outcomes. It works by recursively splitting the dataset based on feature values that maximize information gain or minimize impurity, creating a tree that can make predictions by traversing from root to leaf.

What are ensemble methods? Explain Random Forest.

Ensemble methods combine multiple models to produce better predictions than individual models. Random Forest is an ensemble of decision trees trained on random subsets of data and features. Each tree votes on the prediction, and the final output is determined by majority voting for classification or averaging for regression, reducing overfitting and improving accuracy.

What is the difference between bagging and boosting?

Bagging (Bootstrap Aggregating) trains multiple models independently on random subsets of data and combines their predictions through voting or averaging. Boosting train models sequentially, with each new model focusing on correcting errors made by previous models. Boosting typically achieves higher accuracy but is more prone to overfitting than bagging.

Explain Support Vector Machines (SVM). SVM is a powerful algorithm that finds the optimal hyperplane to separate different classes in the feature space. It maximizes the margin between classes, making it effective for both linear and non-linear classification using kernel tricks. SVMs work well with high-dimensional data and are memory efficient.

What is logistic regression, and when would you use it?

Despite its name, logistic regression is a classification algorithm that predicts probabilities of class membership using the sigmoid function. It's used for binary classification problems and can be extended to multi-class problems. It's interpretable, fast to train, and works well when the relationship between features and log-odds is approximately linear

3. Unsupervised Learning

What is clustering, and name some clustering algorithms.

Clustering is the task of grouping similar data points together without predefined labels. Common algorithms include k-Means (partitions data into k clusters), Hierarchical Clustering (creates a tree of clusters), DBSCAN (density-based clustering that can find arbitrary shapes), and Gaussian Mixture Models (probabilistic clustering).

Explain the k-Means clustering algorithm.

k-Means partitions data into k clusters by iteratively assigning each point to the nearest centroid and then recalculating centroids as the mean of assigned points. This continues until convergence. The algorithm is simple and efficient but requires specifying k beforehand and is sensitive to initial centroid placement and outliers.

What is dimensionality reduction and why is it important?

Dimensionality reduction reduces the number of features in a dataset while preserving important information. It's important for visualizing high-dimensional data, reducing computational costs, avoiding the curse of dimensionality, and removing noise. Common techniques include PCA, t-SNE, and autoencoders.

Explain Principal Component Analysis (PCA).

PCA is a linear dimensionality reduction technique that transforms data into a new coordinate system where the axes (principal components) capture the maximum variance in the data. It identifies orthogonal directions of greatest variance and projects data onto these components, allowing you to reduce dimensions while retaining most information.

What is the difference between PCA and t-SNE?

PCA is a linear technique that preserves global structure and variance, making it fast and suitable for initial exploration. t-SNE is a non-linear technique that preserves local structure and is excellent for visualization, particularly effective at revealing clusters in high-dimensional data. However, t-SNE is computationally expensive and not deterministic.

4. Model Evaluation and Optimization

What is cross-validation and why is it used?

Cross-validation is a technique for assessing how well a model generalizes to unseen data. In k-fold cross-validation, the data is split into k subsets, and the model is trained k times, each time using k-1 folds for training and one fold for validation. This provides a more robust estimate of model performance than a single train-test split.

Explain precision, recall, and F1-score.

Precision is the proportion of predicted positive cases that are actually positive (true positives / (true positives + false positives)). Recall is the proportion of actual positive cases that were correctly identified (true positives / (true positives + false negatives)). F1-score is the harmonic mean of precision and recall, providing a single metric that balances both.

What is the ROC curve and AUC?

The ROC (Receiver Operating Characteristic) curve plots the true positive rate against the false positive rate at various classification thresholds. AUC (Area Under the Curve) measures the entire area under the ROC curve, providing a single metric of model performance across all thresholds. An AUC of 1.0 represents perfect classification, while 0.5 represents random guessing.

What is regularization, and why is it important?

Regularization is a technique to prevent overfitting by adding a penalty term to the loss function that discourages overly complex models. L1 regularization (Lasso) adds the absolute value of coefficients, promoting sparsity. L2 regularization (Ridge) adds the square of coefficients, shrinking weights toward zero. This improves model generalization to new data.

Explain gradient descent and its variants.

Gradient descent is an optimization algorithm that iteratively adjusts model parameters to minimize the loss function by moving in the direction of steepest descent. Batch gradient descent uses the entire dataset, stochastic gradient descent (SGD) uses one sample at a time, and mini-batch gradient descent uses small batches. Variants like Adam and RMSprop use adaptive learning rates for faster convergence.

What is a confusion matrix?

A confusion matrix is a table used to evaluate classification models, showing true positives, true negatives, false positives, and false negatives. It provides a comprehensive view of model performance, revealing not just accuracy but also the types of errors the model makes, which is crucial for understanding model behavior in different scenarios.

How do you handle imbalanced datasets?

Techniques include resampling (oversampling the minority class using methods like SMOTE, or undersampling the majority class), using class weights to penalize misclassifications of the minority class more heavily, choosing appropriate evaluation metrics (precision, recall, F1-score instead of accuracy), and using ensemble methods designed for imbalanced data.

5. Deep Learning Basics

What is a neural network?

A neural network is a computational model inspired by biological neural networks. It consists of interconnected layers of nodes (neurons) that process information. Each connection has a weight that's adjusted during training. Neural networks can learn complex nonlinear relationships through multiple layers of transformations.

Explain forward propagation and backpropagation.

Forward propagation is the process of passing input data through the network layer by layer to generate predictions. Backpropagation calculates the gradient of the loss function with respect to each weight by applying the chain rule, propagating the error backward through the network. These gradients are then used to update weights during training.

What are activation functions, and why are they needed?

Activation functions introduce non-linearity into neural networks, enabling them to learn complex patterns. Without activation functions, a neural network would be equivalent to a linear model regardless of depth. Common activation functions include ReLU (fast and effective), Sigmoid (outputs between 0 and 1), Tanh (outputs between -1 and 1), and Leaky ReLU (addresses dying ReLU problem).

What is the vanishing gradient problem?

The vanishing gradient problem occurs when gradients become extremely small during backpropagation in deep networks, especially with sigmoid or tanh activations. This prevents early layers from learning effectively. Solutions include using ReLU activations, batch normalization, residual connections, and proper weight initialization techniques.

What is the difference between CNN and RNN?

Convolutional Neural Networks (CNNs) are designed for grid-like data such as images, using convolutional layers to detect spatial patterns and hierarchies. Recurrent Neural Networks (RNNs) are designed for sequential data like text or time series, maintaining hidden states that capture information from previous time steps, making them suitable for tasks requiring temporal dependencies.

What is transfer learning?

Transfer learning involves using a pre-trained model on a new but related task, leveraging knowledge learned from one domain to improve performance in another. This is particularly useful when you have limited training data. Common approaches include feature extraction (using pre-trained layers as fixed feature extractors) and fine-tuning (updating pre-trained weights on new data).

6. Practical Machine Learning Questions

How do you handle missing data?

Strategies include deletion (removing rows or columns with missing values if the amount is small), imputation (filling with mean, median, mode, or using more sophisticated methods like k-NN or regression), using algorithms that handle missing values natively, or creating indicator variables to flag missingness. The choice depends on the amount and pattern of missing data.

What is feature engineering, and why is it important?

Feature engineering is the process of creating new features from raw data to improve model performance. It includes scaling, encoding categorical variables, creating interaction terms, polynomial features, and domain-specific transformations. Good feature engineering can dramatically improve model performance and is often more impactful than choosing sophisticated algorithms.

Explain the difference between normalization and standardization.

Normalization (Min-Max scaling) scales features to a fixed range, typically 0 to 1, preserving the shape of the distribution. Standardization (Z-score normalization) transforms features to have zero mean and unit variance, which doesn't bound values but makes different features comparable. Standardization is preferred when the algorithm assumes normally distributed data.

How do you prevent overfitting?

Methods include getting more training data, using simpler models, applying regularization (L1/L2), using dropout in neural networks, early stopping during training, cross-validation, data augmentation, and feature selection to reduce model complexity. Ensemble methods can also help by combining multiple models.

What is the curse of dimensionality?

The curse of dimensionality refers to problems that arise when working with high-dimensional data. As dimensions increase, the volume of the space increases exponentially, making data sparse. This requires exponentially more data to maintain the same density, increases computational cost, and can lead to overfitting and poor generalization.

How would you approach a new machine learning problem?

Start by understanding the business problem and defining success metrics. Explore and visualize the data to understand distributions and relationships. Clean and preprocess data, handling missing values and outliers. Split data into training, validation, and test sets. Start with simple baseline models, then experiment with more complex approaches. Evaluate using appropriate metrics, iterate on feature engineering and model selection, and finally validate on the test set before deployment.

7. Coding and Implementation

Write a function to split data into training and testing sets.

Be prepared to implement or explain basic data preprocessing operations, including train-test splitting with proper randomization and stratification for classification tasks.

How would you implement k-fold cross-validation?

Understand the logic of dividing data into k folds, training on k-1 folds, validating on the remaining fold, and repeating k times to get average performance metrics.

Implement a simple linear regression from scratch.

Know how to calculate coefficients using the normal equation or gradient descent, and understand the mathematical concepts behind linear regression, including cost functions.

Explain how you would use scikit-learn to build a classification model.

Demonstrate familiarity with scikit-learn's workflow, including importing libraries, loading data, splitting datasets, choosing and instantiating models, training with fit(), making predictions with predict(), and evaluating with appropriate metrics.

How do you handle categorical variables in code?

Explain and demonstrate techniques like one-hot encoding using pandas get_dummies or scikit-learn's OneHotEncoder, label encoding for ordinal variables, and target encoding for high-cardinality categorical features.

Tips for Machine Learning Interview Success

Understand Fundamentals: Don't just memorize answers. Understand the underlying concepts, mathematics, and intuition behind algorithms.

Practice Coding: Be comfortable implementing basic ML algorithms and using popular libraries like scikit-learn, pandas, and NumPy.

Work on Projects: Having hands-on projects demonstrates practical experience. Be ready to discuss your projects in detail, including challenges faced and how you solved them.

Stay Updated: Machine learning evolves rapidly. Follow recent developments, new architectures, and emerging techniques in the field.

Conclusion

Preparing for machine learning interviews requires a solid understanding of fundamentals, practical experience, and the ability to communicate complex concepts clearly. This guide covers the essential topics, but remember that interviews vary by company and role. Focus on understanding concepts deeply rather than memorizing answers, work on projects to gain practical experience, and practice explaining your thought process clearly.

The field of machine learning is vast and constantly evolving, so maintain a growth mindset and continue learning. Good luck with your interviews in 2026!