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Quantiphi Analytics Solutions Private Limited Machine Learning Engineer Interview Questions and Answers
12 Interview questions
L1 and L2 regularizations are techniques to prevent overfitting in machine learning models by adding penalties to the loss function.
L1 regularization (Lasso) adds the absolute value of coefficients to the loss function, promoting sparsity.
L2 regularization (Ridge) adds the squared value of coefficients, which discourages large weights but retains all features.
L1 can lead to feature selection by driving some coeffi...
Overfitting occurs when a model learns the training data too well, leading to poor generalization. Underfitting happens when a model is too simple to capture the underlying patterns.
Overfitting: Model performs well on training data but poorly on unseen data. Can be caused by a model being too complex or training for too long.
Underfitting: Model is too simple to capture the underlying patterns in the data. Results ...
To overcome overfitting, use techniques like cross-validation, regularization, early stopping, and increasing training data.
Use cross-validation to evaluate model performance on different subsets of data.
Apply regularization techniques like L1 or L2 regularization to penalize large coefficients.
Implement early stopping to stop training when validation error starts to increase.
Increase training data to provide more...
Transformers are a type of neural network architecture designed for processing sequential data, particularly in NLP tasks.
Transformers use self-attention mechanisms to weigh the importance of different words in a sentence.
They consist of an encoder-decoder structure, where the encoder processes input data and the decoder generates output.
Transformers can handle long-range dependencies better than RNNs due to their...
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 c...
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...
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 traini...
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 Baye...
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 pr...
PCA is a dimensionality reduction technique used to reduce the number of features in a dataset while preserving the most important information.
PCA stands for Principal Component Analysis
It works by finding the directions (principal components) in which the data varies the most
These principal components are orthogonal to each other and capture the maximum variance in the data
Feature selection can be done by selecti...
Quantiphi Analytics Solutions Private Limited Machine Learning Engineer Interview Experiences
16 interviews found
Anonymous
I applied via LinkedIn and was interviewed in Apr 2024. There were 2 interview rounds.
(4 Questions)
- Q1. In depth questions on every ml algorithm from supervised to un supervised algorithms
- Q2. How to over come over fitting
- Ans.
To overcome overfitting, use techniques like cross-validation, regularization, early stopping, and increasing training data.
Use cross-validation to evaluate model performance on different subsets of data.
Apply regularization techniques like L1 or L2 regularization to penalize large coefficients.
Implement early stopping to stop training when validation error starts to increase.
Increase training data to provide more dive...
- Q3. What is PCA, how to do feature selection
- Ans.
PCA is a dimensionality reduction technique used to reduce the number of features in a dataset while preserving the most important information.
PCA stands for Principal Component Analysis
It works by finding the directions (principal components) in which the data varies the most
These principal components are orthogonal to each other and capture the maximum variance in the data
Feature selection can be done by selecting th...
- Q4. Project based questions
(1 Question)
- Q1. Scenario based questions on implementing NLP use case
Skills evaluated in this interview
Selected
I applied via Campus Placement and was interviewed in Jun 2024. There were 2 interview rounds.
Contain question related to aptitude, pyrhon,ml mcqs.
(2 Questions)
- Q1. What is overfitting and underfitting?
- Ans.
Overfitting occurs when a model learns the training data too well, leading to poor generalization. Underfitting happens when a model is too simple to capture the underlying patterns.
Overfitting: Model performs well on training data but poorly on unseen data. Can be caused by a model being too complex or training for too long.
Underfitting: Model is too simple to capture the underlying patterns in the data. Results in po...
- Q2. Explain about your project
Skills evaluated in this interview
I applied via Campus Placement
78 mcq questions with 2 coding questions in 1 hr 26 min.
(2 Questions)
- Q1. Don't know about it.
- Q2. I was not selected.
Aptitude round - Quant reasoning verbal
Three coding questions were given
(1 Question)
- Q1. Questions from project and some ml questions
(1 Question)
- Q1. This was like technical and hr Questions from project
Interview Preparation Tips
I applied via Campus Placement and was interviewed in Dec 2023. There were 2 interview rounds.
Big data, aptitude, html, css, javascript, c language questions
Hackerrank coding test
Rohan Tandalekar
Selected
I appeared for an interview before Jun 2024, where I was asked the following questions.
- Q1. What are L1 and L2 regularizations?
- Ans.
L1 and L2 regularizations are techniques to prevent overfitting in machine learning models by adding penalties to the loss function.
L1 regularization (Lasso) adds the absolute value of coefficients to the loss function, promoting sparsity.
L2 regularization (Ridge) adds the squared value of coefficients, which discourages large weights but retains all features.
L1 can lead to feature selection by driving some coefficient...
- Q2. Explain Gradient descent.
- Ans.
Gradient descent is an optimization algorithm used to minimize a function by iteratively moving towards the steepest descent.
Gradient descent updates parameters in the opposite direction of the gradient of the loss function.
The learning rate determines the size of the steps taken towards the minimum.
There are different variants: Batch Gradient Descent, Stochastic Gradient Descent (SGD), and Mini-batch Gradient Descent.
...
Selected
I applied via Campus Placement and was interviewed before Oct 2022. There were 4 interview rounds.
OOP, aptitude, DSA, coding question-2 mcq and 1 code based
(4 Questions)
- Q1. Pythons-OOP,DSA, basic questions
- Q2. Polymorphism, inheritance
- Q3. OOPs, 4 pillars of OOPs
- Ans.
OOPs stands for Object-Oriented Programming and its 4 pillars are Inheritance, Encapsulation, Abstraction, and Polymorphism.
Inheritance allows a class to inherit properties and behavior from another class.
Encapsulation restricts access to certain components of an object, protecting its integrity.
Abstraction hides complex implementation details and only shows the necessary features.
Polymorphism allows objects to be trea...
- Q4. Dsa and python- Arrays,Stack, linked list,etc
(1 Question)
- Q1. HR and technical question about ml, dl, cut the cake in 8 parts how will you do it.
Interview Preparation Tips
Selected
I applied via Campus Placement and was interviewed before Sep 2022. There were 4 interview rounds.
Questions on Quantitative, English and Logic.
Next section comprised of questions on SQL, OS, Java, HTML/CSS.
Last section had questions related to python and Machine learning and also couple of Coding questions whose level was Easy to moderate.
(1 Question)
- Q1. Name some evaluation metrics? What is precision and recall? Give some examples. What is Entropy and Gini impurity What are bagging techniques What are boosting techniques Difference between validation and ...
- Ans.
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 p...
(1 Question)
- Q1. Tell me about yourself Where do you see yourself in next 5 years Why you want to join our company Soft skill questions like leadership, innovative thinker, problem solver etc. which are mentioned in the re...
Interview Preparation Tips
Skills evaluated in this interview
Selected
I applied via Superset and was interviewed before May 2022. There were 2 interview rounds.
(2 Questions)
- Q1. Actually after internship we will have evaluation.
- Ans. So in evaluation they decide whether to continue or to extend your 4 month of internship. I got converted into FTE after 4 months.
- Q2. You need to showcase what u did in the internship As I converted to FTE from internship
- Ans. U need to submit ur trakstar(evaluation) And u need to keep. Meet with mentor and they will rate you
Interview Preparation Tips
- Complete the internship
- Machine Learning
- Deep Learning
If they feel u need more improvement they will extend ur internship period.
Try to complete your task in internship, learn faster.
Be proactive with mentors.
Participate in all activities in org.
Take this for learning and applying what u have learned.
Selected
I applied via Campus Placement and was interviewed before Apr 2023. There was 1 interview round.
(1 Question)
- Q1. Question on DSA, basics of Machine Learning and puzzles from gfg
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