70+ Ai Ml Engineer Interview Questions and Answers

Developed a sentiment analysis model using natural language processing techniques.

• Used Python and libraries like NLTK and Scikit-learn for data preprocessing and model building

• Collected and cleaned a dataset of customer reviews from an e-commerce website

• Implemented a bag-of-words model and trained a logistic regression classifier

• Evaluated the model's performance using metrics like accuracy, precision, and recall

Yes, I have experience with cloud computing and would approach building the architecture by leveraging scalable cloud services like AWS or Azure.

• Utilize cloud services like AWS or Azure for scalability and flexibility

• Implement containerization using Docker for easy deployment and management

• Use serverless computing for cost efficiency and automatic scaling

• Leverage managed AI services like AWS SageMaker or Azure Machine Learning for AI/ML tasks

NumPy and Pandas are used to manipulate data in Python, with NumPy focusing on numerical operations and Pandas on data manipulation and analysis.

• NumPy is used for numerical operations like array manipulation, mathematical functions, and linear algebra.

• Pandas is used for data manipulation tasks like data cleaning, merging, reshaping, and analysis.

• NumPy arrays can be easily converted to Pandas DataFrames for more advanced data manipulation and analysis.

• Example: Using NumPy to p...read more

Python basics include list comprehensions, decorators, and object-oriented programming principles.

• List comprehensions provide a concise way to create lists in Python. Example: squares = [x**2 for x in range(10)]

• Decorators are functions that modify the behavior of other functions. Example: @my_decorator def my_function():

• Object-oriented programming principles in Python involve classes, objects, inheritance, and polymorphism.

Machine learning is a subset of artificial intelligence that involves the development of algorithms and models that enable computers to learn from and make predictions or decisions based on data.

• Machine learning is a subset of artificial intelligence that focuses on the development of algorithms and models that allow computers to learn from and make predictions or decisions based on data.

• There are three main types of machine learning: supervised learning, unsupervised learnin...read more

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

Answering questions related to SQL concepts and syntax.

• Projects in SQL refer to selecting specific columns from a table.

• View in SQL is a virtual table created by a query.

• Union combines the results of two or more SELECT statements, while Union All includes duplicates.

• Referential integrity is maintained through foreign key constraints.

• Types of triggers in SQL include DML triggers and DDL triggers.

• Where clause is used to filter rows based on a specified condition, while Having c...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

Generative AI refers to AI models that can generate new content, such as images, text, or music. LLM stands for Large Language Models, which are AI models trained on vast amounts of text data.

• Generative AI can be used to create realistic images, generate human-like text, or compose music.

• LLMs like GPT-3 are trained on large datasets to understand and generate human language.

• Generative AI has applications in creative fields, chatbots, and content generation.

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.

As n tends to infinity in binomial distribution, it approaches a normal distribution.

• As n tends to infinity, the shape of the binomial distribution curve becomes more symmetrical.

• The mean and variance of the binomial distribution approach the mean and variance of a normal distribution.

• The central limit theorem states that as n approaches infinity, the distribution of sample means approaches a normal distribution.

Transformer is a deep learning model that uses self-attention mechanism. Multi Head Attention is a variant of attention mechanism. Positional Encoding is used to inject information about the position of tokens in the sequence.

• Transformer is a deep learning model introduced in the paper 'Attention is All You Need' by Vaswani et al.

• Multi Head Attention allows the model to focus on different parts of the input sequence simultaneously.

• Positional Encoding is added to the input emb...read more

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

Iterator is an object that allows iteration over a collection, while iterable is an object that can be iterated over.

• Iterator is an object with a next() method that returns the next item in the collection.

• Iterable is an object that has an __iter__() method which returns an iterator.

• Example: List is iterable, while iter(list) returns an iterator.

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

Embeddings are a way to represent words or phrases as vectors in a high-dimensional space, capturing semantic relationships.

• Embeddings are important for tasks like natural language processing, where words need to be represented in a meaningful way.

• They can be created using techniques like Word2Vec, GloVe, or using neural networks like Word Embeddings.

• Embeddings help in capturing semantic relationships between words, allowing models to understand context and meaning.

• For exampl...read more

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

RAG stands for Retrieve, Aggregate, and Group. Data can be retrieved from Vector DB using SQL queries.

• RAG is a process used to retrieve, aggregate, and group data from a database.

• SQL queries are commonly used to retrieve data from Vector DB.

• Example: SELECT * FROM table_name WHERE condition;

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

Techniques to optimize transformer models include pruning, distillation, quantization, and knowledge distillation.

• Pruning: Removing unnecessary parameters to reduce model size and improve efficiency.

• Distillation: Training a smaller student model to mimic the behavior of a larger teacher model.

• Quantization: Reducing the precision of weights and activations to speed up inference.

• Knowledge distillation: Transferring knowledge from a large model to a smaller one for faster infere...read more

Encoder-decoder is a neural network architecture used for tasks like machine translation.

• Encoder processes input data and generates a fixed-length representation

• Decoder uses the representation to generate output sequence

• Example: Seq2Seq model for translating English to French

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.

Polymorphism allows objects of different classes to be treated as objects of a common superclass. Generators produce items one at a time. Decorators add functionality to existing functions or methods.

• Polymorphism: Enables objects of different classes to be treated as objects of a common superclass. Example: Animal superclass with Dog and Cat subclasses.

• Generator: Produces items one at a time, allowing for efficient memory usage. Example: range() function in Python.

• Decorator: ...read more

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

Code to remove duplicates from list without using set

• Iterate through the list and compare each element with the rest of the elements

• If a duplicate is found, remove it from the list

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

Retrieval augmentation generation is a technique used in natural language processing to improve the quality of generated responses by combining retrieval-based and generation-based methods.

• Retrieval augmentation generation involves retrieving relevant information from a knowledge base or corpus and then using this information to generate a response.

• It combines the benefits of retrieval-based methods (which provide accurate and factual information) with generation-based method...read more

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

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']