50+ Ai Ml Engineer Interview Questions and Answers

DL architectures like CNNs require more computing power due to their complex structure and operations.

• The architecture of Ai Ml is simpler compared to other DL architectures like CNNs.

• It uses a single layer of neurons which reduces the number of computations required.

• It also uses a linear activation function which is computationally less expensive.

• Ai Ml is suitable for simpler tasks like linear regression and classification.

• In contrast, CNNs are used for complex tasks like im...read more

Creating a chat bot in Python involves several steps such as defining intents, training the model, and integrating with messaging platforms.

• Define intents and entities for the chat bot using a natural language processing library like spaCy or NLTK.

• Train the chat bot model using a machine learning framework like TensorFlow or PyTorch.

• Integrate the chat bot with messaging platforms like Facebook Messenger or Slack using APIs.

• Continuously improve the chat bot's performance by co...read more

Use a hashmap to store the difference between target and each element, then check if the current element exists in the hashmap.

• Create a hashmap to store the difference between target and each element in the array.

• Iterate through the array and check if the current element exists in the hashmap.

• Return the pair of numbers that add up to the target.

Some libraries used for web automation are Selenium, Puppeteer, and Playwright.

• Selenium is a popular choice for automating web browsers across many platforms.

• Puppeteer is a Node library which provides a high-level API to control Chrome or Chromium over the DevTools Protocol.

• Playwright is a Node library for automating browsers, including Chromium, Firefox, and WebKit.

Symmetric encryption uses the same key for both encryption and decryption, while asymmetric encryption uses different keys for encryption and decryption.

• Symmetric encryption is faster and more efficient than asymmetric encryption.

• Asymmetric encryption provides better security as it uses a public key for encryption and a private key for decryption.

• Examples of symmetric encryption algorithms include AES and DES, while examples of asymmetric encryption algorithms include RSA and...read more

CNN is a deep learning algorithm commonly used for image classification. YOLOV5 is a real-time object detection model. MAP is mean average precision.

• CNN stands for Convolutional Neural Network, which uses convolutional layers to extract features from images for tasks like image classification.

• YOLOV5 is a popular object detection model that divides an image into a grid and predicts bounding boxes and class probabilities for objects within each grid cell.

• MAP (Mean Average Preci...read more

I have a strong understanding of Python and JavaScript, with experience in developing AI/ML applications using both languages.

• Proficient in Python for data manipulation, machine learning algorithms, and AI model development

• Skilled in JavaScript for front-end development and building interactive web applications

• Experience using Python libraries like NumPy, Pandas, Scikit-learn, TensorFlow, and Keras

• Familiar with JavaScript frameworks like React, Angular, and Node.js

I am familiar with various learning models and algorithms such as linear regression, logistic regression, decision trees, random forests, support vector machines, k-nearest neighbors, neural networks, and deep learning.

• Linear regression

• Logistic regression

• Decision trees

• Random forests

• Support vector machines

• K-nearest neighbors

• Neural networks

• Deep learning

I would approach a machine learning problem by first understanding the problem, collecting and preprocessing data, selecting a suitable algorithm, training the model, evaluating its performance, and fine-tuning it.

• Understand the problem statement and define the objectives clearly.

• Collect and preprocess the data to make it suitable for training.

• Select a suitable machine learning algorithm based on the problem type (classification, regression, clustering, etc.).

• Train the model ...read more

MobileNet is a lightweight deep learning model designed for mobile and embedded devices.

• MobileNet uses depthwise separable convolutions to reduce the number of parameters and computations.

• It has a small memory footprint and can be easily deployed on mobile and embedded devices.

• MobileNet has been used for various applications such as image classification, object detection, and semantic segmentation.

• It has achieved state-of-the-art performance on several benchmark datasets.

• Mobi...read more

Mobilenet is a lightweight DL architecture designed for mobile devices.

• Mobilenet uses depthwise separable convolutions to reduce computation and model size.

• It has fewer parameters and lower computational requirements compared to other DL architectures like VGG and ResNet.

• Mobilenet is optimized for mobile devices and can run in real-time on smartphones and other embedded devices.

• Other DL architectures like VGG and ResNet are designed for high accuracy on large datasets and req...read more

F1 score is a metric used to evaluate the performance of a classification model by considering both precision and recall.

• F1 score is the harmonic mean of precision and recall, calculated as 2 * (precision * recall) / (precision + recall).

• It is a better metric than accuracy when dealing with imbalanced datasets.

• A high F1 score indicates a model with both high precision and high recall.

• F1 score ranges from 0 to 1, where 1 is the best possible score.

Supervised learning uses labeled data to train a model, while unsupervised learning uses unlabeled data to find patterns.

• Supervised learning requires a target variable for training

• Unsupervised learning finds hidden patterns in data without a target variable

• Examples of supervised learning include classification and regression tasks

• Examples of unsupervised learning include clustering and dimensionality reduction

RNNs are used for sequential data while LSTMs are better for long-term dependencies.

• Use RNNs for tasks like language modeling, speech recognition, and time series prediction.

• Use LSTMs when dealing with long sequences and tasks requiring memory of past inputs.

• LSTMs are more suitable for tasks like machine translation, sentiment analysis, and text generation.

Supervised learning uses labeled data to train a model, while unsupervised learning finds patterns in unlabeled data.

• Supervised learning requires input-output pairs for training

• Examples include linear regression, support vector machines, and neural networks

• Unsupervised learning clusters data based on similarities or patterns

• Examples include k-means clustering, hierarchical clustering, and principal component analysis

I am currently working on developing machine learning models using Python, TensorFlow, and scikit-learn.

• Python programming language

• TensorFlow framework

• scikit-learn library

det(A- lamda*I) = 0 represents the characteristic equation of a matrix A.

• It represents finding the eigenvalues of matrix A.

• It is used to solve for the values of lambda that satisfy the equation.

• The equation is used to determine the behavior of the matrix under certain transformations.

• Example: For a 2x2 matrix A, det(A- lamda*I) = 0 gives a quadratic equation to solve for eigenvalues.

Developed a recommendation system for an e-commerce platform using collaborative filtering

• Used collaborative filtering to analyze user behavior and recommend products

• Implemented matrix factorization techniques to improve recommendation accuracy

• Evaluated model performance using metrics like RMSE and precision-recall curves

Hyperplane is used in SVM to separate classes in a high-dimensional space.

• Hyperplane separates classes in SVM

• Maximizes margin between classes

• Can be linear or nonlinear depending on kernel function

Count the frequency of each element in a list

• Iterate through the list and use a dictionary to store the count of each element

• If the element is already in the dictionary, increment its count by 1

• Return the dictionary with element frequencies

I have used Tesseract, Google Cloud Vision, and Amazon Textract for OCR tasks.

• Tesseract is an open-source OCR engine that I have used for various projects.

• Google Cloud Vision API provides powerful OCR capabilities and integration with other Google services.

• Amazon Textract is a machine learning-based OCR service that I have used for extracting text from scanned documents.

Supervised, Unsupervised, Reinforcement Learning

• Supervised Learning: Uses labeled data to train the model (e.g. linear regression, decision trees)

• Unsupervised Learning: Finds patterns in unlabeled data (e.g. clustering, dimensionality reduction)

• Reinforcement Learning: Learns through trial and error by receiving rewards or penalties (e.g. Q-learning, Deep Q Networks)

Python function to calculate cosine similarity between two vectors.

• Define a function that takes two vectors as input.

• Calculate the dot product of the two vectors.

• Calculate the magnitude of each vector and multiply them.

• Divide the dot product by the product of magnitudes to get cosine similarity.

OHE is a technique used in machine learning to convert categorical data into a binary format.

• OHE is used to convert categorical variables into a format that can be provided to ML algorithms.

• Each category is represented by a binary vector where only one element is 'hot' (1) and the rest are 'cold' (0).

• For example, if we have a 'color' feature with categories 'red', 'blue', 'green', OHE would represent them as [1, 0, 0], [0, 1, 0], [0, 0, 1] respectively.

Probabilistic distributions describe the likelihood of different outcomes in a random variable.

• Probabilistic distributions show the probability of each possible outcome in a random variable

• Common types include normal, binomial, Poisson, and exponential distributions

• They are often used in statistics, machine learning, and AI to model uncertainty and make predictions

Supervised machine learning is a type of machine learning where the model is trained on labeled data.

• In supervised learning, the algorithm learns from labeled training data.

• The model makes predictions based on input data and is corrected by comparing it to the actual output.

• Examples include classification and regression tasks.

• Popular algorithms for supervised learning include linear regression, logistic regression, support vector machines, and decision trees.

NLP (Natural Language Processing) in machine learning is the ability of a computer to understand, interpret, and generate human language.

• NLP enables machines to analyze and derive meaning from human language data.

• It involves tasks such as text classification, sentiment analysis, named entity recognition, and machine translation.

• Examples of NLP applications include chatbots, language translation services, and speech recognition systems.

MobileNet is a lightweight deep learning architecture designed for mobile and embedded vision applications.

• MobileNet uses depthwise separable convolutions to reduce the number of parameters and computations.

• It has a small memory footprint and can be easily deployed on mobile devices.

• MobileNet has several variants, including MobileNetV1, MobileNetV2, and MobileNetV3.

• MobileNetV2 introduced linear bottlenecks and inverted residuals to improve accuracy and efficiency.

• MobileNetV3 ...read more

Cosine similarity is a measure of similarity between two non-zero vectors in an inner product space.

• It measures the cosine of the angle between the two vectors.

• Values range from -1 (completely opposite) to 1 (exactly the same).

• Used in recommendation systems, text mining, and clustering algorithms.

Conditional probability is the likelihood of an event occurring given that another event has already occurred.

• Conditional probability is calculated using the formula P(A|B) = P(A and B) / P(B)

• It represents the probability of event A happening, given that event B has already occurred

• Conditional probability is used in various fields such as machine learning, statistics, and finance

Linear regression is used for continuous variables, while logistic regression is used for binary classification.

• Linear regression predicts continuous values, while logistic regression predicts probabilities between 0 and 1.

• Linear regression uses a linear equation to model the relationship between the independent and dependent variables.

• Logistic regression uses the logistic function to model the probability of a binary outcome.

• Linear regression is suitable for predicting house...read more

Reverse an array of strings without using methods

• Create two pointers, one at the start and one at the end of the array

• Swap the elements at the two pointers and move them towards the center until they meet

• Example: ['apple', 'banana', 'cherry'] -> ['cherry', 'banana', 'apple']

Developed a sentiment analysis model using NLP to analyze customer reviews for a product.

• Collected and preprocessed text data from various sources

• Performed tokenization, stopword removal, and lemmatization

• Built a machine learning model using techniques like TF-IDF and LSTM

• Evaluated the model's performance using metrics like accuracy and F1 score

• Deployed the model for real-time sentiment analysis of new reviews

SQL query to find Set difference between two tables.

• Use the EXCEPT keyword in SQL to find the set difference between two tables.

• The EXCEPT keyword returns rows from the first table that are not present in the second table.

• Example: SELECT column_name FROM table1 EXCEPT SELECT column_name FROM table2;

Join is a SQL operation used to combine rows from two or more tables based on a related column between them.

• Types of joins include INNER JOIN, LEFT JOIN, RIGHT JOIN, and FULL JOIN.

• INNER JOIN returns rows when there is at least one match in both tables.

• LEFT JOIN returns all rows from the left table and the matched rows from the right table.

• RIGHT JOIN returns all rows from the right table and the matched rows from the left table.

• FULL JOIN returns rows when there is a match in o...read more

Opps in Python refers to Object Oriented Programming concepts and principles.

• Opps in Python allows for creating classes and objects to organize code and data

• Encapsulation, inheritance, and polymorphism are key concepts in Opps in Python

• Example: defining a class 'Car' with attributes like 'color' and methods like 'drive'

ML should be used when there is a large amount of data and complex patterns to be analyzed.

• Use ML when there is a need to make predictions or decisions based on data

• ML is suitable for tasks like image recognition, natural language processing, and recommendation systems

• Avoid using ML for simple rule-based tasks that can be easily programmed

• Consider the trade-offs between accuracy and interpretability when deciding to use ML

Code in SQL to remove duplicates from a table

• Use the DISTINCT keyword in a SELECT statement to retrieve unique rows

• Use a temporary table to store the unique rows and then replace the original table with the temporary table

The P value is a statistical measure that helps determine the significance of results in hypothesis testing.

• P value is the probability of obtaining results at least as extreme as the observed results, assuming the null hypothesis is true.

• A small P value (typically ≤ 0.05) indicates strong evidence against the null hypothesis, leading to its rejection.

• A large P value (> 0.05) suggests weak evidence against the null hypothesis, leading to its acceptance.

• P values help researcher...read more

Types of prompts include text prompts, image prompts, audio prompts, and video prompts.

• Text prompts: prompts that are in written form

• Image prompts: prompts that are in visual form

• Audio prompts: prompts that are in audio form

• Video prompts: prompts that are in video form

Precision and recall are evaluation metrics used in machine learning to measure the performance of a classification model.

• Precision is the ratio of correctly predicted positive observations to the total predicted positive observations.

• Recall is the ratio of correctly predicted positive observations to the all observations in actual class.

• Precision is important when the cost of false positives is high, while recall is important when the cost of false negatives is high.

• F1 score...read more

Vision Transformers are a type of neural network architecture that applies the transformer model to image data.

• Vision Transformers break down images into smaller patches and process them using transformer layers.

• They have shown promising results in image classification tasks, surpassing traditional convolutional neural networks in some cases.

• Examples of vision transformers include ViT (Vision Transformer) and DeiT (Data-efficient Image Transformer).

Lemmatization is the process of reducing words to their base or root form.

• Lemmatization helps in standardizing words for analysis.

• It reduces inflected words to their base form.

• For example, 'running' becomes 'run' after lemmatization.

Machine learning is a subset of artificial intelligence that focuses on developing algorithms and models that can learn from and make predictions or decisions based on data.

• Machine learning involves training algorithms to learn patterns and make predictions from data.

• It can be supervised, unsupervised, or semi-supervised learning.

• Examples include image recognition, natural language processing, and recommendation systems.

I typically use a combination of machine learning algorithms, data preprocessing techniques, and model evaluation methods in my workflow.

• Utilize various machine learning algorithms such as linear regression, random forests, and neural networks

• Preprocess data using techniques like normalization, feature scaling, and handling missing values

• Evaluate models using metrics like accuracy, precision, recall, and F1 score

Random forest is an ensemble learning method used for classification and regression tasks.

• Random forest is made up of multiple decision trees.

• Each tree in the forest is trained on a random subset of the training data.

• During prediction, each tree gives a vote for the most popular class (in classification) or average prediction (in regression).

• The final prediction is determined by aggregating the individual tree predictions.

• Random forest is known for its high accuracy and abili...read more

I have 3 years of experience in data science, including working on projects involving predictive modeling and machine learning algorithms.

• Developed predictive models using regression and classification algorithms

• Implemented machine learning algorithms for pattern recognition

• Utilized data visualization techniques to communicate insights

• Worked with large datasets to extract meaningful information