100+ Machine Learning Engineer Interview Questions and Answers

Over-fitting is when a model is too complex and fits the training data too well, while under-fitting is when a model is too simple and cannot capture the underlying patterns in the data.

• Over-fitting occurs when a model is trained too much on the training data and starts to memorize it instead of learning the underlying patterns.

• Under-fitting occurs when a model is too simple and cannot capture the complexity of the data.

• To deal with over-fitting, one can use techniques such a...read more

Python uses automatic memory management through garbage collection.

• Python uses reference counting to keep track of object references.

• When an object's reference count reaches zero, it is automatically deallocated.

• Python also employs a garbage collector to handle cyclic references.

• The 'gc' module provides control over the garbage collector.

• Memory management can be optimized using techniques like object pooling and memory profiling.

Different types of learning include supervised learning, unsupervised learning, semi-supervised learning, reinforcement learning, and transfer learning.

• Supervised learning: Training a model using labeled data to make predictions or classifications.

• Unsupervised learning: Training a model on unlabeled data to discover patterns or relationships.

• Semi-supervised learning: Combining labeled and unlabeled data for training.

• Reinforcement learning: Training a model to make decisions b...read more

Explanation of evaluation metrics, precision, recall, entropy, Gini impurity, bagging, boosting, validation vs test data, LSTM, GRU, K-means clustering, and importing CSV datasets.

• Evaluation metrics: used to measure the performance of machine learning models (e.g., accuracy, precision, recall, F1 score)

• Precision: ratio of true positive predictions to the total predicted positives (TP / (TP + FP))

• Recall: ratio of true positive predictions to the total actual positives (TP / (T...read more

Label encoding and one-hot encoding are techniques used to convert categorical data into numerical data.

• Label encoding assigns a unique numerical value to each category in a feature.

• One-hot encoding creates a binary vector for each category in a feature.

• Label encoding is useful when the categories have an inherent order or hierarchy.

• One-hot encoding is useful when the categories are unordered or when the number of categories is small.

• One-hot encoding can lead to a high-dimens...read more

ML algorithms are techniques used to train models to make predictions or decisions based on data.

• Supervised learning algorithms: Linear regression, logistic regression, decision trees, random forests, support vector machines, k-nearest neighbors

• Unsupervised learning algorithms: K-means clustering, hierarchical clustering, principal component analysis

• Reinforcement learning algorithms: Q-learning, SARSA

• Deep learning algorithms: Convolutional neural networks, recurrent neural ne...read more

Preprocess image data while maintaining ratios and register multiple models in Django.

• Resize images while maintaining aspect ratio using libraries like PIL or OpenCV

• Normalize pixel values to a range of 0-1 for better model performance

• Augment data using techniques like rotation, flipping, or cropping to increase dataset size

• Use data generators in Keras to efficiently load and preprocess images in batches

• Register multiple models in Django by creating separate model classes in m...read more

A docker image is a lightweight, standalone, executable package that includes everything needed to run an application.

• Docker images are created using a Dockerfile which contains instructions for building the image.

• Images can be stored in a registry and pulled to run on any machine with Docker installed.

• To check running containers, use the command 'docker ps' which lists all running containers.

• To see all containers, including stopped ones, use 'docker ps -a'.

AWS S3 has 6 storage classes: S3 Standard, S3 Intelligent-Tiering, S3 Standard-Infrequent Access, S3 One Zone-Infrequent Access, S3 Glacier, and S3 Glacier Deep Archive.

• S3 Standard: for frequently accessed data

• S3 Intelligent-Tiering: automatically moves data to the most cost-effective tier

• S3 Standard-Infrequent Access: for long-lived, infrequently accessed data

• S3 One Zone-Infrequent Access: for infrequently accessed data that can be recreated

• S3 Glacier: for long-term archival...read more

Design a system to extract insights from customer service chat data

• Implement natural language processing (NLP) techniques to analyze text data

• Use sentiment analysis to understand customer emotions and satisfaction levels

• Identify frequently asked questions or common issues to improve customer service

• Create visualizations to present key insights and trends to stakeholders

Python function to check if a number is palindrome or not.

• Convert the number to a string

• Reverse the string

• Compare the reversed string with the original string

• Return True if they are equal, else False

Use natural language processing techniques to detect and identify medicines on a doctor's prescription.

• Preprocess the text by removing noise and irrelevant information.

• Tokenize the text to break it down into individual words or characters.

• Use a language model or dictionary to match the tokens with known medicines.

• Apply machine learning algorithms like NER (Named Entity Recognition) to identify medicine names.

• Consider context and surrounding words to improve accuracy of predic...read more

Metrics for regression problems

• Mean Squared Error (MSE)

• Root Mean Squared Error (RMSE)

• Mean Absolute Error (MAE)

• R-squared (R²) score

• Coefficient of Determination

• Explained Variance Score

Python function to check if a number is prime or not

• Check if number is less than 2, return False

• Check if number is divisible by any number from 2 to its square root, return False

• Else, return True

To deal with skewed distribution of a variable, transformations like log, square root, or box-cox can be applied.

• Apply log transformation to reduce right skewness

• Apply square root transformation to reduce left skewness

• Apply box-cox transformation for a more generalized approach

• Consider removing outliers before applying transformations

Designed a feed ranking system using collaborative filtering and content-based filtering techniques.

• Utilized collaborative filtering to recommend items based on user behavior and preferences.

• Incorporated content-based filtering to recommend items based on their attributes and characteristics.

• Implemented a hybrid approach combining collaborative and content-based filtering for improved recommendations.

• Used machine learning algorithms such as matrix factorization, k-nearest nei...read more

Chose Yolo for its real-time processing speed and simplicity compared to two-stage detectors.

• Yolo is faster than two-stage detectors as it processes the image in a single pass

• Yolo is simpler to implement and train compared to two-stage detectors like Faster R-CNN

• Yolo is more suitable for real-time applications where speed is crucial, such as autonomous driving or video surveillance

Variance in principal component analysis helps to identify the most important features in the data.

• Variance measures the spread of data points around the mean, indicating the importance of each feature in capturing the overall variability.

• Higher variance implies more information is retained by the principal components, making them more significant in representing the data.

• By selecting components with high variance, we can reduce the dimensionality of the data while preserving...read more

Null hypothesis is a statement that assumes no relationship or difference between variables. P-value is the probability of obtaining results as extreme as the observed data, assuming the null hypothesis is true.

• Null hypothesis is a statement that assumes no effect or relationship between variables

• P-value is the probability of obtaining results as extreme as the observed data, assuming the null hypothesis is true

• Null hypothesis is typically denoted as H0, while an alternative ...read more

Yes, Decision tree is a supervised learning algorithm and Random Forest is an ensemble learning method.

• Decision tree is a tree-like model where each internal node represents a test on an attribute, each branch represents the outcome of the test, and each leaf node represents a class label.

• Random Forest is a collection of decision trees where each tree is built using a random subset of the training data and a random subset of the features.

• Random Forest reduces overfitting and ...read more

Outliers are data points that deviate significantly from the rest of the data. They can be handled by removing, transforming or imputing them.

• Outliers can be detected using statistical methods like Z-score, IQR, or visual methods like box plots.

• Removing outliers can lead to loss of information, so transforming or imputing them is preferred.

• Transforming outliers can be done by applying mathematical functions like log, square root, or inverse.

• Imputing outliers can be done by re...read more

Use the pandas library to read a CSV file in Python.

• Import the pandas library: import pandas as pd

• Use the read_csv() function to read the CSV file: df = pd.read_csv('file.csv')

• Specify additional parameters like delimiter, header, etc. if needed

I have worked on projects related to image recognition, natural language processing, and predictive analytics using machine learning.

• Developed a deep learning model for image recognition using convolutional neural networks

• Implemented a sentiment analysis system using natural language processing techniques

• Built a predictive analytics model for customer churn prediction in a telecom company

Naive Bayes is a probabilistic algorithm that uses Bayes' theorem to classify data based on prior knowledge.

• Naive Bayes assumes that all features are independent of each other.

• It is commonly used for text classification and spam filtering.

• There are three types of Naive Bayes classifiers: Gaussian, Multinomial, and Bernoulli.

• It is a fast and simple algorithm that works well with high-dimensional datasets.

• Naive Bayes can handle missing data and is not affected by irrelevant fea...read more

args and kwargs are special syntax in Python used to pass a variable number of arguments to a function.

• args is used to pass a variable number of non-keyword arguments to a function

• kwargs is used to pass a variable number of keyword arguments to a function

• args is represented by an asterisk (*) and kwargs is represented by two asterisks (**)

• args and kwargs can be used together in a function definition

• Example: def my_func(*args, **kwargs):

I prefer TensorFlow because of its flexibility, scalability, and community support.

• TensorFlow is widely used and has a large community, making it easy to find resources and support.

• It offers a wide range of tools and libraries for building and deploying machine learning models.

• TensorFlow's graph-based approach allows for easy scalability and distributed computing.

• It also has strong support for both deep learning and traditional machine learning.

• Other popular frameworks includ...read more

iLoc is used for integer-location based indexing while Loc is used for label-based indexing in pandas.

• iLoc is used for selecting data based on integer index positions.

• Loc is used for selecting data based on labels.

• iLoc uses integer index positions starting from 0.

• Loc uses labels from the index or column names.

• Example: df.iloc[0] selects the first row based on integer index position.

• Example: df.loc['row_label'] selects the row with label 'row_label'.

A food ordering system like Swiggy allows users to browse restaurants, place orders, track delivery, and make payments online.

• User registration and login functionality

• Restaurant listing with menu and prices

• Cart management for adding/removing items

• Order tracking and status updates

• Payment gateway integration

• Delivery tracking with real-time updates

I have worked on projects involving natural language processing, computer vision, and predictive modeling.

• Developed a sentiment analysis model using NLP techniques

• Implemented a facial recognition system using computer vision algorithms

• Built a predictive model for customer churn prediction

Develop a recommendation system for ranking developers for job positions.

• Collect data on developer skills, experience, projects, and job preferences

• Use collaborative filtering to recommend job positions based on similar developers

• Implement content-based filtering to recommend jobs based on developer skills and preferences

• Utilize machine learning algorithms to continuously improve recommendations

• Consider incorporating feedback from developers and employers to enhance the syste...read more

The answer depends on the size of the plane and the size of the tennis balls.

• The size of the plane and the size of the tennis balls are important factors to consider.

• The packing method used to fit the tennis balls in the plane also matters.

• Assuming a standard commercial plane and tennis ball size, approximately 50,000 tennis balls can fit in the plane.

Transformer architecture is a neural network architecture used for natural language processing tasks. Positional encoders are used to encode the position of words in a sentence.

• Transformer architecture is based on the self-attention mechanism.

• It consists of an encoder and a decoder.

• Positional encoders are added to the input embeddings to encode the position of words in a sentence.

• They are computed using sine and cosine functions of different frequencies.

• Positional encoders he...read more

Vanishing gradient and dead activation are common problems in deep neural networks.

• Vanishing gradient occurs when the gradient becomes too small during backpropagation, making it difficult for the network to learn.

• Dead activation happens when a neuron always outputs the same value, causing it to have no effect on the network's output.

• Both problems can occur in deep networks with many layers, especially when using certain activation functions like sigmoid or tanh.

• Solutions to ...read more

CNN stands for Convolutional Neural Network, used for image classification and object recognition.

• CNN is a type of neural network that uses convolutional layers to extract features from images.

• It is commonly used for image classification and object recognition tasks.

• CNNs can have multiple layers, including convolutional, pooling, and fully connected layers.

• The number of layers used depends on the complexity of the task and the size of the dataset.

• In my case, I used a CNN with...read more

Designing an end-to-end machine learning system involves multiple components working together to process data, train models, and make predictions.

• 1. Data collection and preprocessing: Gather relevant data and clean, transform, and prepare it for training.

• 2. Model training: Use algorithms to train machine learning models on the preprocessed data.

• 3. Model evaluation: Assess the performance of the trained models using metrics like accuracy, precision, and recall.

• 4. Deployment: I...read more