Top 40 Interview Questions on Machine Learning

The demand for machine learning engineers is extremely high and continues to grow rapidly because of the AI/ML revolution, data explosion, automation and efficiency, and skill shortage. Top companies are actively seeking ML engineers to develop and implement AI/ML solutions. Here are the top 40 interview questions on machine learning for beginners and experienced professionals. If you are new to it, check out our machine learning course syllabus.

Machine Learning Interview Questions for Freshers

Here are the machine learning basic questions for freshers.

What is Machine Learning and how it works?

The goal of machine learning (ML), a branch of artificial intelligence (AI), is to allow systems to learn from their experiences and get better without explicit programming. This entails analyzing data, finding trends, and formulating predictions or judgments using statistical models and algorithms. 

• A lot of data is used to train machine learning algorithms.
• The algorithms discover patterns and connections by learning from the data.
• This data is used by the algorithms to forecast and decide.
• As the algorithms are exposed to more data over time, their performance gets better.

What is Principal Component Analysis (PCA)?

The data is reduced in dimensionality by converting it into a new collection of uncorrelated variables known as principal components, which account for the majority of the data’s variation. 

What is a Convolutional Neural Network (CNN)?

A kind of deep neural network frequently used for processing and image identification. Convolutional layers are used by CNNs to extract features from pictures.

What is a Recurrent Neural Network (RNN)?

RNN is a kind of neural network made to handle sequential data, such as natural language or time series. Because of their memory capacities, RNNs may take historical data into account.

What is Gradient Descent?

The loss function is minimized using an optimization technique. The model’s parameters are iteratively changed in the direction of the loss function’s steepest descent.

What is the difference between a parameter and a hyperparameter?

Parameter: A parameter is a variable (such as weights in a neural network) that is discovered from the training data during the model training process.

Hypermeter: A hyperparameter is a variable (such as learning rate or the number of hidden layers) that is specified prior to the start of the training process and governs the learning process.

What is a loss function used for?

The discrepancy between the model’s predictions and the actual values is measured by a loss function. By showing how effectively or poorly the model is performing, it directs the learning process and enables the model to modify its parameters in order to minimize the loss.

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What is Cross-Validation?

This method involves splitting the data into several folds, training the model on a subset of the folds, and then assessing the model’s performance on the remaining fold. This helps in evaluating how well the model generalizes to new data. 

Explain Overfitting and Underfitting.

Overfitting: When a model performs very well on training data but poorly on unseen data, this is known as overfitting. The model is too complicated and unable to generalize since it has learned the noise in the training data.

Underfitting: When a model is underfit, it performs poorly on both training and test data because it is unable to identify the underlying patterns in the data. The model cannot successfully learn from the data because it is too simplistic. 

What distinguishes unsupervised anomaly detection from supervised anomaly detection?

Supervised anomaly detection: Trains a model to differentiate between typical and unusual occurrences using labeled data.

Unsupervised anomaly detection: Finds patterns or departures from expected behavior in unlabeled data to identify anomalies.

What is Backpropagation?

An algorithm for neural network training. It determines the loss function’s gradient in relation to the model’s parameters and modifies them appropriately.

What is a Gradient Vanishing/Exploding Problem?

Problems that may arise in deep neural networks during backpropagation.

• Vanishing gradients: As gradients move through several layers, they get progressively smaller, which hinders the network’s ability to learn.
• Exploding gradients: When gradients get too big, training becomes erratic and may even diverge.

What is the trade-off between bias and variance?

• Bias: Systematic error brought on by the learning algorithm’s oversimplified assumptions. Underfitting may result from high bias.
• Variance: The model’s sensitivity to training data. Overfitting may result from high volatility.
• The tradeoff: To attain the best model performance, strike a compromise between bias and variance.

What does regularization aim to achieve?

by including a penalty term in the loss function, overfitting can be avoided. This promotes the model’s learning of more straightforward and broadly applicable representations. L1 and L2 regularization are popular regularization methods.

What distinguishes L1 regularization from L2 regularization?

By adding the weights’ absolute values to the loss function, L1 regularization (Lasso) tends to create sparse models (many weights become zero).

By adding the weights’ squared value to the loss function, L2 regularization (Ridge) tends to generate models with fewer weights.

What is K-Means Clustering?

Data points are divided into K clusters using an unsupervised learning technique, and each data point is assigned to the cluster with the closest mean (centroid). 

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What is the Elbow Method?

Plotting the within-cluster sum of squares (WCSS) against various values of K and determining the “elbow” point at which the rate of reduction in WCSS begins to diminish is a method used to estimate the ideal number of clusters (K) in K-Means clustering. 

What is a recommendation system?

A program that makes tailored suggestions based on user preferences (e.g., product recommendations, movie recommendations).

What is a Confusion Matrix?

A table that displays the quantity of true positives, true negatives, false positives, and false negatives in order to provide an overview of a classification model’s performance. 

• A confusion matrix contrasts a dataset’s expected and actual values.
• The number of right and wrong predictions is displayed in a 2D matrix.
• It is employed to assess a classification model’s performance. 

What is a Neural Network?

A computer model with layers of interconnected nodes (neurons) that was modeled after the structure of the human brain. Complex patterns and correlations in data can be learned using neural networks.

Applications of Neural Network:

• Image recognition: CNNs are capable of identifying objects, classes, and categories in images.
• Time series prediction: Using sequential data, such as time series data, RNNs can forecast future values.
• Natural language processing: Sequential data, such as words and sentences, can be processed using RNNs.  

Machine Learning Interview Questions and Answers for Experienced

Here are the commonly asked machine learning questions for interview:

What is a Deep Neural Network?

A deep neural network (DNN) is a type of machine learning model that processes data and resolves challenging issues by utilizing several layers of artificial neurons. An artificial neural network (ANN) type called a DNN is made to replicate the way the human brain absorbs and processes information. Multiple hidden layers in a neural network enable it to learn increasingly intricate data representations.

Applications of Deep Neural Network:

• DNNs are utilized in computer vision to recognize images.
• Natural language processing: Natural language processing is done with DNNs.
• Health: DNNs are employed to identify several illnesses, including cancer.
• DNNs are utilized in aviation to maximize airline fleets.
• DNNs are utilized in the oil and gas industry for machine predictive maintenance.
• Finance: Fraud detection is done with DNNs.

What is the purpose of an Activation Function?

A neural network can learn intricate patterns that linear models are unable to capture by introducing non-linearity through the use of an activation function. 

Examples of activation functions:

• ReLU (Rectified Linear Unit): It is a popular, straightforward function that substitutes 0 for negative numbers. It helps prevent gradient problems and is computationally efficient.
• Leaky ReLU: ReLU has been improved by adding a tiny positive slope to the negative region. This lessens the possibility of overfitting.
• Tanh (Hyperbolic Tangent): With an output range of -1 to 1, it is comparable to a sigmoid. Compared to sigmoid, its gradient is higher.
• Softmax: A nonlinear function that turns each class’s raw scores into probability. Usually, neural networks use it in the output layer.

What are the popular applications of machine learning?

Here are the common applications of machine learning:

• Speech recognition: Converts spoken words into printed language
• Computer vision: Obtains useful information by analyzing pictures and movies.
• Recommendation engines: Create cross-selling tactics based on historical consumption data
• Fraud detection: Financial organizations can detect suspicious transactions with the aid of fraud detection.
• Image compression: Makes data files smaller to increase storage capacity.
• Weather prediction: Learn the connection between rainfall and weather patterns using machine learning.
• Travel time estimation: This method makes use of machine learning to determine how long a trip will take. 

What is the difference between Accuracy, Precision, Recall, and F1-score?

Percentage: The percentage of accurate forecasts among all predictions is known as accuracy.

• It determines the percentage of accurate guesses among all of the forecasts made.
• When one class is noticeably larger than the other in an unbalanced dataset, it might be deceptive. 

Precision: The percentage of actual positive forecasts among all positive forecasts.

• It calculates the percentage of optimistic forecasts that come true.
• It provides a response to the query, “How many of all the instances the model predicted as positive were truly positive?” 

Recall: The proportion of actual positive events that were correctly predicted to be positive.

• It calculates the percentage of real positive examples that the model accurately detects.
• It responds to the query, “How many did the model correctly identify out of all the actual positive instances?” 

F1-score: A balance between precision and recall, calculated as the harmonic mean of the two. 

• It is determined by taking the harmonic mean of recall and precision, which yields a single measure that strikes a balance between the two.
• It is especially helpful when you need to take into account both factors and the cost of false positives and false negatives is comparable. 

What are the different types of Machine Learning?

Supervised Learning: Training models using labeled data, where the input data and matching output are supplied, is known as supervised learning.

Examples: Regression and classification (such as logistic regression, support vector machines, and decision trees) are two examples.

Unsupervised Learning: In unsupervised learning, the model looks for hidden patterns and structures in unlabeled data.

Examples: Dimensionality Reduction (e.g., Principal Component Analysis, or PCA) and Clustering (e.g., k-means).

Reinforcement Learning: An agent that interacts with its surroundings and learns to respond in a way that maximizes a reward signal is said to be engaging in reinforcement learning.

Examples: Deep Q-Networks (DQN) and Q-learning.

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How can the Gradient Vanishing/Exploding Problem be solved?

• Weight initialization: These issues can be lessened with careful weight initialization.
• Activation functions: Vanishing gradients can be avoided by using activation functions such as ReLU.
• Batch normalization: By normalizing the inputs to every layer, batch normalization increases the stability of training.
• Gradient clipping: Limiting the size of gradients during backpropagation is known as gradient trimming.

What distinguishes mini-batch gradient descent, stochastic gradient descent, and batch gradient descent from one another?

• Batch Gradient Descent: In each iteration, batch gradient descent determines the gradient of the loss function for the complete training dataset.
• Stochastic Gradient Descent: In each iteration, stochastic gradient descent determines the gradient of the loss function for a single training case.
• Mini-batch Gradient Descent: In each iteration, the gradient of the loss function for a tiny subset (mini-batch) of the training data is determined using the mini-batch gradient descent method.

Which methods are frequently used while creating recommendation systems?

Collaborative filtering: It finds related persons or objects by taking advantage of user-item interactions (such as ratings and purchases).

Content-based filtering: Makes suggestions for products that are comparable to ones a user has previously enjoyed.

Hybrid strategies: Integrate content-based and collaborative filtering.

What is Natural Language Processing (NLP)?

An area of artificial intelligence that focuses on how computers and human language interact, including tasks like question answering, machine translation, sentiment analysis, and text classification.

What is Text Classification?

Text classification into predetermined classifications (e.g., sentiment analysis, spam detection). The technique of classifying text into distinct classes using artificial intelligence (AI) and machine learning (ML) is known as text classification. Other names for it include text categorization and text tagging.  

What is Sentiment Analysis?

Identifying the emotional tone (positive, negative, or neutral) that is conveyed in a text. The act of examining digital text to ascertain if the message’s emotional tone is neutral, negative, or positive is known as sentiment analysis. These days, businesses have a lot of text data, such as emails, chat logs from customer service, comments on social media, and reviews. 

In machine learning, what function does feature engineering serve?

By choosing, creating, and modifying pertinent variables from the raw data, feature engineering in machine learning effectively shapes the data into a format that best fits the selected machine learning algorithm, increasing its accuracy and performance. This process turns raw data into meaningful features that a model can use to make predictions. To enhance model performance, pertinent features are chosen, extracted, and transformed from raw data.

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Why is data preparation important for machine learning?

To guarantee the consistency and quality of the data, which might have a big influence on model performance, data preparation is essential. 

• Data cleaning: It is addressing missing values, eliminating outliers, and resolving inconsistencies are examples of common preprocessing activities.
• Data transformation: It is categorical variable scaling, normalization, and encoding.
• Data reduction: PCA and other dimensionality reduction methods.

What are some typical machine learning problems?

• Data quality: Handling biased, noisy, and incomplete data.
• Overfitting and underfitting: Striking the correct balance between model complexity and overfitting and underfitting.
• Interpretability: The capacity to comprehend and elucidate the choices made by intricate models.
• Scalability: Using big datasets to train and implement models.
• Ethical consideration: Ensuring accountability, privacy, and equity in machine learning systems are ethical considerations.

Which methods are frequently used to discover anomalies?

• Statistical methods: Z-score and interquartile range-based outlier detection.
• Clustering: Finding data points that are distant from the cluster centers is known as clustering.
• One-class SVM: A model is trained to recognize the limits of typical data points using a one-class SVM.
• Autoencoders: Teach a neural network to rebuild typical data and use reconstruction mistakes to detect abnormalities.

What part does domain knowledge play in machine learning?

• Feature engineering: The selection and development of pertinent features can be guided by domain experts.
• Data interpretation: Domain knowledge aids in deciphering and comprehending the consequences of model results.
• Problem definition: Accurate problem definition and reasonable goal-setting depend on domain expertise.

What distinguishes a Random Forest from a Decision Tree?

• Decision Tree: A straightforward, tree-like model that divides data according to attributes in order to make judgments.
• Random Forest: An ensemble learning technique that reduces overfitting and increases prediction accuracy by combining several decision trees. 

What is a Support Vector Machine (SVM)?

A supervised machine learning approach called a Support Vector Machine (SVM) divides data into classes by determining the most effective line or hyperplane. SVMs can be applied to regression and multi-class classification, however they are most frequently employed to address binary classification issues.

• SVMs convert input data into a space with more dimensions.
• The hyperplane they discover maximizes the separation between the nearest data points of various classifications.
• Support vectors are the lines that pass through the data points to calculate the maximum margin.
• The margin is the separation between the nearest data points and the hyperplane. 

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What does dimensionality reduction aim to achieve?

This can shorten training times, enhance model performance, and facilitate data visualization. Its primary goal is to reduce the amount of features in a dataset while preserving important information. 

• Simplifies data: Particularly for large datasets, dimensionality reduction facilitates data processing and analysis.
• Improves model performance: Reducing dimensionality can result in machine learning models that are more accurate and efficient.
• Helps with visualization: Data representations with fewer dimensions are simpler to see and understand.
• Helps with data compression: Reducing dimensionality can help data use less storage space.

What are the key points in feature engineering?

Key points in feature engineering are:

• Transforming raw data: Raw data is transformed by extracting pertinent features that can be utilized to train a model, which facilitates the algorithm’s ability to find patterns and connections in the data.
• Feature selection: Choosing which features are most crucial for the prediction task and eliminating those that are superfluous or unnecessary is known as feature selection.
• Feature creation: Feature creation is the process of creating new features from the data by merging preexisting ones or using mathematical adjustments to extract new information.
• Handling missing data: To make sure the model can manage partial data, missing values should be imputed using the proper techniques.
• Scaling and normalization: To enhance the performance of specific algorithms, the range of features is adjusted to a common scale. 

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Conclusion

This comprehensive list of 40 interview questions on Machine Learning along with answers covers a wide range of fundamental and advanced concepts. It aims to help you prepare effectively for technical interviews with problem-solving skills, field expertise, strong foundation on algorithms, and practical applications. If you want to upgrade your skills, enroll in our machine learning course in Chennai.