80+ ml engineer Interview Questions and Answers

Regression models can be evaluated using R squared and adjusted R squared to measure the goodness of fit.

• R squared measures the proportion of the variance in the dependent variable that is predictable from the independent variables.

• Adjusted R squared adjusts for the number of predictors in the model, providing a more accurate measure of goodness of fit.

• R squared can be artificially inflated by adding more predictors, while adjusted R squared penalizes for adding unnecessary v...read more

Explanation of bagging, boosting, ensemble models, handling overfitting, and precision-recall-ROC curve.

• Bagging (Bootstrap Aggregating) involves training multiple models on different subsets of the training data and combining their predictions through averaging or voting.

• Boosting involves training multiple models sequentially, with each model correcting the errors of its predecessor.

• Ensemble models combine multiple individual models to improve overall performance and generali...read more

Decision trees are a popular machine learning algorithm used for classification and regression tasks.

• Decision trees are a flowchart-like structure where each internal node represents a feature or attribute, each branch represents a decision rule, and each leaf node represents the outcome.

• To select parent and child nodes, the algorithm calculates the best split at each node based on criteria like Gini impurity or information gain.

• Gini impurity is a measure of how often a rando...read more

Correlation measures the strength and direction of a relationship between two variables. A correlation of 0 indicates no linear relationship.

• Correlation measures the degree to which two variables move in relation to each other. It ranges from -1 to 1.

• A correlation of 0 means there is no linear relationship between the variables. They are not related in a linear fashion.

• For example, if the correlation between hours of study and exam scores is 0, it means there is no linear rel...read more

Stochastic gradient descent, batch gradient descent, and gradient descent differ in the amount of data used to update the model weights.

• Gradient descent updates the model weights using the entire dataset in each iteration.

• Batch gradient descent updates the model weights using a subset of the dataset (batch) in each iteration.

• Stochastic gradient descent updates the model weights using only one data point at a time in each iteration.

• Stochastic gradient descent is faster but mor...read more

LLMs are large neural network models used for natural language processing tasks.

• Parameters include model size, number of layers, attention mechanisms, and training data size.

• Use cases include text generation, translation, summarization, and sentiment analysis.

• Examples of LLMs are GPT-3, BERT, and XLNet.

Regularization techniques help prevent overfitting in machine learning models. Ridge regression adds L2 regularization, while Lasso regression adds L1 regularization.

• Regularization techniques help prevent overfitting by adding a penalty term to the loss function.

• Ridge regression adds the squared magnitude of coefficients as penalty term (L2 regularization).

• Lasso regression adds the absolute magnitude of coefficients as penalty term (L1 regularization).

• Ridge regression tends t...read more

The decision between wider or deeper neural networks depends on the complexity of the data and the trade-off between computational resources and performance.

• Consider the complexity of the data: Deeper networks are better for more complex data, while wider networks are better for simpler data.

• Evaluate computational resources: Deeper networks require more computational resources and training time compared to wider networks.

• Experiment with both architectures: Try training models...read more

Monitor and train a LLM by tracking performance metrics, updating data, retraining model, and implementing feedback loops.

• Track performance metrics such as accuracy, precision, recall, and F1 score to monitor model performance.

• Update training data regularly to keep the model up-to-date with new information and trends.

• Retrain the model periodically using the updated data to improve its performance and adapt to changes.

• Implement feedback loops to continuously improve the model ...read more

Metrics used to evaluate a LLM and decide to fine tune it

• Accuracy

• Precision and Recall

• F1 Score

• Confusion Matrix

• ROC Curve and AUC

To ensure no duplicate images, we can use image hashing techniques to compare and identify similar images.

• Compute image hashes using algorithms like perceptual hashing (e.g., pHash)

• Compare the hashes of all images to identify duplicates

• Consider using image similarity metrics to set a threshold for similarity

• Apply clustering algorithms to group similar images together

• Leverage machine learning models to classify and detect duplicate images

Object detection identifies objects in an image, while segmentation assigns a label to each pixel in the image.

• Object detection involves identifying and locating objects within an image, often using bounding boxes.

• Segmentation assigns a class label to each pixel in the image, creating a pixel-wise mask for each object.

• Object detection is typically used when the goal is to identify and locate multiple objects in an image, while segmentation is used for pixel-level understandin...read more

Developed a predictive model to identify potential customer churn for a telecom company

• Performed exploratory data analysis to identify key features affecting customer churn

• Preprocessed data by handling missing values and encoding categorical variables

• Built and compared various machine learning models including logistic regression, decision tree, and random forest

• Tuned hyperparameters using grid search and cross-validation

• Achieved an accuracy of 85% and identified key factors ...read more

Precision is the ratio of correctly predicted positive observations to the total predicted positive observations, while Recall is the ratio of correctly predicted positive observations to the all observations in actual class.

• Precision focuses on the accuracy of positive predictions, while Recall focuses on the proportion of actual positives that were correctly identified.

• Precision = TP / (TP + FP)

• Recall = TP / (TP + FN)

• Precision is important when the cost of false positives i...read more

Handle missing values in Kmeans cluster by imputing with mean, median, or mode.

• Impute missing values with mean, median, or mode of the feature

• Consider using algorithms like KNN imputation or MICE for more complex cases

• Drop rows or columns with missing values if they are insignificant in number

• Normalize data before imputing missing values to avoid bias

Python code to find prime numbers in a given range

• Iterate through the range of numbers

• Check if each number is divisible by any number other than 1 and itself

• If not divisible, then it is a prime number

Designing a complete MLOps pipeline with all the necessary steps.

• Data collection and preprocessing

• Model training and evaluation

• Model deployment

• Monitoring and feedback loop

• Automated retraining

• Version control and collaboration

Confusion metrics are used to evaluate the performance of a classification model by measuring the accuracy of predictions.

• Confusion matrix is a table that describes the performance of a classification model.

• It shows the number of true positives, true negatives, false positives, and false negatives.

• From the confusion matrix, various metrics like accuracy, precision, recall, and F1 score can be calculated.

• These metrics help in understanding how well the model is performing and ...read more

Hyperparameters of Random Forest include number of trees, max depth of trees, minimum samples per leaf, and maximum features.

• Number of trees: Determines the number of decision trees in the forest.

• Max depth of trees: Controls the maximum depth of each decision tree.

• Minimum samples per leaf: Specifies the minimum number of samples required to be at a leaf node.

• Maximum features: Determines the maximum number of features to consider when looking for the best split.

Implement a sorting algorithm to sort a list without using built-in functions.

• Use a common sorting algorithm like bubble sort, selection sort, or insertion sort.

• Iterate through the list and compare adjacent elements to swap them if necessary.

• Repeat the process until the list is sorted in ascending order.

I have experience in deploying and managing applications on AWS cloud platform.

• Deployed and managed a web application on AWS Elastic Beanstalk

• Used AWS Lambda for serverless computing

• Configured and managed EC2 instances for various projects

• Used S3 for storing and retrieving data

• Set up and managed RDS instances for databases

• Used CloudFormation for infrastructure as code

• Implemented auto-scaling and load balancing for high availability

• Used CloudWatch for monitoring and logging

• Imp...read more

The technical approach for a problem involves defining the problem, gathering data, selecting algorithms, training models, evaluating performance, and iterating.

• Define the problem statement and objectives clearly

• Gather relevant data and preprocess it for analysis

• Select appropriate algorithms and techniques based on the problem

• Train machine learning models using the data

• Evaluate model performance using metrics like accuracy, precision, recall

• Iterate on the model by tuning hype...read more

Handling imbalance in Naive Bayes involves using techniques like oversampling, undersampling, or using different evaluation metrics.

• Use oversampling techniques like SMOTE to create synthetic samples of the minority class.

• Use undersampling techniques to randomly remove samples from the majority class.

• Adjust class weights in the Naive Bayes algorithm to give more importance to the minority class.

• Use different evaluation metrics like F1 score or ROC-AUC instead of accuracy when ...read more

K value for KNN algo can be selected using techniques like cross-validation, grid search, and elbow method.

• Use cross-validation to find the optimal K value by splitting the data into training and validation sets multiple times.

• Perform grid search by testing a range of K values and selecting the one with the highest accuracy.

• Apply the elbow method by plotting the K values against the error rate and selecting the K value where the error rate starts to stabilize.

• Consider the tra...read more

Delete removes a record entirely, while update modifies an existing record.

• Delete removes the entire record from the database

• Update modifies specific fields of an existing record

• Delete is irreversible, while update can be undone by another update

• Example: Deleting a user account vs updating the user's email address

Delete removes rows one by one, while truncate removes all rows at once.

• Delete is a DML command and can be rolled back, while truncate is a DDL command and cannot be rolled back.

• Delete triggers delete triggers and fires delete triggers, while truncate does not trigger any triggers.

• Delete is slower as it logs individual row deletions, while truncate is faster as it logs the deallocation of the data pages.

• Delete can have a WHERE clause to specify which rows to delete, while tru...read more

PCA is a dimensionality reduction technique that transforms data into a lower-dimensional space. Feature selection is the process of selecting a subset of relevant features for use in model training.

• PCA helps in reducing the dimensionality of data by finding the principal components that explain the most variance in the data.

• Feature selection involves selecting the most important features from the dataset based on certain criteria like correlation, importance scores, or domai...read more

Data imbalance can be addressed by resampling techniques like oversampling, undersampling, or using algorithms like SMOTE.

• Use oversampling to increase the number of minority class samples.

• Use undersampling to decrease the number of majority class samples.

• Utilize techniques like SMOTE (Synthetic Minority Over-sampling Technique) to generate synthetic samples for the minority class.

• Consider using ensemble methods like Random Forest or XGBoost which can handle imbalanced data we...read more

Training a Multi-head Yolov9 model involves optimizing the model architecture for detecting clothes in images.

• Utilize transfer learning with pre-trained Yolov9 model for faster convergence

• Fine-tune the model on a dataset of clothes images to improve detection accuracy

• Adjust hyperparameters such as learning rate, batch size, and optimizer for optimal performance

LLM stands for Large Language Models, which are AI models trained on vast amounts of text data to understand and generate human language.

• LLMs use deep learning techniques to process and understand language data.

• Some popular LLMS include GPT-3 (Generative Pre-trained Transformer 3) by OpenAI and BERT (Bidirectional Encoder Representations from Transformers) by Google.

• LLMs have been used in various applications such as natural language processing, text generation, chatbots, and...read more

PCA stands for Principal Component Analysis. It is a dimensionality reduction technique used to reduce the number of variables in a dataset while preserving the most important information.

• PCA is used to transform high-dimensional data into a lower-dimensional space by finding the principal components that explain the maximum variance in the data.

• The first principal component is the direction in which the data varies the most, followed by the second principal component, and so...read more

Eigenvectors are used in PCA and SVD to find the directions of maximum variance in a dataset.

• Eigenvectors in PCA represent the principal components, which are the directions of maximum variance in the data.

• Eigenvectors in SVD are used to decompose a matrix into singular values and left and right singular vectors.

• PCA uses eigenvectors to transform the data into a new coordinate system where the axes are the principal components.

• SVD uses eigenvectors to find the basis vectors t...read more

ML workflow involves data collection, preprocessing, model training, evaluation, and deployment.

• Data collection: Gather relevant data from various sources.

• Data preprocessing: Clean, transform, and prepare data for model training.

• Model training: Develop and train machine learning models using algorithms.

• Evaluation: Assess model performance using metrics like accuracy, precision, recall.

• Deployment: Implement the model into production environment for real-world use.

Stack, queue, and BST are data structures used in computer science for storing and organizing data.

• Stack: Last in, first out data structure. Examples include undo functionality in text editors.

• Queue: First in, first out data structure. Examples include printer queues.

• BST (Binary Search Tree): Hierarchical data structure where each node has at most two children. Used for efficient searching and sorting.

I have extensive experience in both Machine Learning and Deep Learning.

• I have worked on various machine learning projects, including image classification, natural language processing, and recommendation systems.

• I am proficient in popular machine learning libraries such as TensorFlow and PyTorch.

• I have implemented deep learning models like convolutional neural networks (CNNs) and recurrent neural networks (RNNs) for tasks like image recognition and sequence prediction.

• I have e...read more

Bias-variance tradeoff is the balance between underfitting and overfitting in machine learning models.

• Bias refers to error from erroneous assumptions in the learning algorithm, leading to underfitting.

• Variance refers to error from sensitivity to fluctuations in the training data, leading to overfitting.

• The tradeoff involves finding the right level of model complexity to minimize both bias and variance.

• Regularization techniques like Lasso and Ridge regression can help in manag...read more

Support vector machine is a supervised machine learning algorithm used for classification and regression tasks.

• SVM finds the hyperplane that best separates the classes in the feature space

• It works well for high-dimensional data and is effective in cases with clear margin of separation

• Can handle non-linear data by using kernel trick to map data into higher dimensional space

• Popular kernels include linear, polynomial, radial basis function (RBF)

Gradient descent is an optimization algorithm used to minimize the error of a model by adjusting its parameters iteratively.

• Gradient descent starts with random initial parameters and calculates the gradient of the cost function with respect to each parameter.

• It then updates the parameters in the opposite direction of the gradient to minimize the cost function.

• This process is repeated iteratively until the algorithm converges to the optimal parameters.

• Learning rate determines ...read more

Confusion metrics are used to evaluate the performance of a classification model by comparing predicted values with actual values.

• Calculate True Positives (TP), False Positives (FP), True Negatives (TN), and False Negatives (FN)

• Use these values to calculate metrics like accuracy, precision, recall, and F1 score

• Confusion matrix can be visualized using tools like matplotlib or seaborn

HAVING is used with GROUP BY to filter groups, WHERE is used to filter rows

• HAVING is used with GROUP BY clause to filter groups based on aggregate functions

• WHERE is used to filter rows based on conditions

• HAVING is applied after GROUP BY, WHERE is applied before GROUP BY

• Example: SELECT department, AVG(salary) FROM employees GROUP BY department HAVING AVG(salary) > 50000

• Example: SELECT * FROM employees WHERE age > 30

Transformer is a neural network architecture based on self-attention mechanism.

• Consists of encoder and decoder layers

• Uses self-attention mechanism to weigh input tokens

• Employs positional encoding to capture token positions

• Introduced in the 'Attention is All You Need' paper by Vaswani et al.

• Popularly used in natural language processing tasks

LLAMA models are used for large language model adaptation.

• LLAMA models are used for adapting large language models to specific tasks or domains.

• They are commonly used in natural language processing tasks such as text generation, translation, and sentiment analysis.

• Examples of LLAMA models include GPT-3, BERT, and RoBERTa.

Tuning data parameters involves adjusting various settings to optimize the performance of a machine learning model.

• Identify relevant parameters to tune based on the specific model and dataset

• Use techniques like grid search, random search, or Bayesian optimization to find the best parameter values

• Evaluate the model performance using metrics like accuracy, precision, recall, and F1 score

• Iteratively adjust parameters and retrain the model to improve performance

MSE stands for Mean Squared Error, a common metric used to measure the average squared difference between predicted values and actual values.

• MSE is calculated by taking the average of the squared differences between predicted and actual values.

• It is commonly used in machine learning to evaluate the performance of regression models.

• Lower MSE values indicate better model performance, as they represent smaller errors between predicted and actual values.

• Example: If actual values ...read more

CI/CD pipeline automates the process of testing and deploying code changes.

• CI/CD pipeline helps in automating the process of integrating code changes, running tests, and deploying the code to production.

• It ensures that code changes are tested thoroughly before being deployed, reducing the chances of bugs or errors in production.

• Example: A CI/CD pipeline for a machine learning model would involve automatically running tests on the model's performance metrics, retraining the mo...read more

Filters in CNN are small matrices used to extract features from input data by performing convolution operations.

• Filters are applied to small regions of the input data to detect specific patterns or features.

• Each filter slides over the input data and performs element-wise multiplication followed by summation to produce a feature map.

• Filters are learned during the training process to capture important features for the task at hand.

• Common filter sizes are 3x3 or 5x5, and multipl...read more

SVM stands for Support Vector Machine, a supervised machine learning algorithm used for classification and regression tasks.

• SVM finds the hyperplane that best separates different classes in the feature space

• It can handle both linear and non-linear data by using different kernel functions

• SVM is widely used in image classification, text classification, and bioinformatics