100+ Machine Learning Engineer Interview Questions and Answers

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.

I will analyze the real-world data and try to identify the reasons for the poor performance.

• Check if the real-world data is different from the training and validation data

• Analyze the features and identify if any important features are missing

• Check if the model is overfitting on the training data

• Try to collect more real-world data to improve the model's performance

• Consider using a different model or algorithm

• Perform hyperparameter tuning to optimize the model's performance

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

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

The model is not working due to overfitting, lack of data, and incorrect features. Steps to improve include regularization, data augmentation, and feature engineering.

• Overfitting: Model may be too complex for the data, use regularization techniques like L1/L2 regularization or dropout.

• Lack of data: Collect more data or use data augmentation techniques like flipping, rotating, or adding noise to existing data.

• Incorrect features: Perform feature engineering to create more relev...read more

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

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

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

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

To deploy a model on edge, consider model optimization, hardware compatibility, deployment framework, and monitoring.

• Optimize the model for edge deployment by reducing size and complexity.

• Ensure the model is compatible with the edge device's hardware specifications.

• Choose a deployment framework suitable for edge computing, such as TensorFlow Lite or ONNX.

• Implement monitoring and logging mechanisms to track model performance and errors on the edge device.

AWS SageMaker DataWrangler is a data preparation service that helps to clean and normalize data for machine learning.

• It provides a visual interface to explore, transform, and combine data from various sources.

• It supports a wide range of data formats and can handle missing or inconsistent data.

• It generates code in Python or PySpark for reproducibility and scalability.

• It integrates with other AWS services like SageMaker Studio and Glue for end-to-end ML workflows.

Metrics for evaluating machine learning tasks

• Accuracy

• Precision

• Recall

• F1 Score

• ROC AUC

• Confusion Matrix

I used the Random Forest algorithm for the task.

• Random Forest is an ensemble learning method that builds multiple decision trees and merges them together to get a more accurate and stable prediction.

• It is commonly used for classification and regression tasks.

• Example: RandomForestClassifier in scikit-learn library.

Evaluation metrics for classification are used to assess the performance of a classification model.

• Common evaluation metrics include accuracy, precision, recall, F1 score, and ROC-AUC.

• Accuracy measures the proportion of correctly classified instances out of the total instances.

• Precision measures the proportion of true positive predictions out of all positive predictions.

• Recall measures the proportion of true positive predictions out of all actual positive instances.

• F1 score i...read more

Regression is a statistical method used to analyze the relationship between a dependent variable and one or more independent variables.

• Regression is used to predict continuous numerical values.

• It helps in identifying the strength and direction of the relationship between variables.

• Linear regression is a common type of regression used to model the relationship between two variables.

• Examples of regression include predicting housing prices based on square footage and predicting ...read more