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Feynn Labs Data Science Intern Interview Questions, Process, and Tips
Top Feynn Labs Data Science Intern Interview Questions and Answers
- Q1. What is linear regression and logistics regression?
- Q2. What is central limit theorem? Why we use it
- Q3. What is svm? Any project you perform using this?
Feynn Labs Data Science Intern Interview Experiences
5 interviews found
Anonymous
Selected
I was interviewed in Jul 2024.
(2 Questions)
- Q1. What is random forest? What it is called random?
- Q2. What is svm? Any project you perform using this?
Python question
SQL queries
Form filling paid
(4 Questions)
- Q1. Difference between Random and ordering partition
- Ans.
Random partition involves splitting data randomly, while ordering partition involves splitting data based on a specific order.
Random partition randomly divides data into subsets without any specific order.
Ordering partition divides data into subsets based on a specific order, such as time or alphabetical order.
Random partition is useful for creating training and testing sets for machine learning models.
Ordering partiti...
- Q2. Difference between Logistic and Linear Regression
- Ans.
Logistic regression is used for binary classification while linear regression is used for regression tasks.
Logistic regression predicts the probability of a binary outcome (0 or 1) based on one or more independent variables.
Linear regression predicts a continuous outcome based on one or more independent variables.
Logistic regression uses a sigmoid function to map predicted values between 0 and 1, while linear regressio...
- Q3. Difference between KNN and K Means
- Ans.
KNN is a supervised learning algorithm used for classification and regression, while K Means is an unsupervised clustering algorithm.
KNN stands for K-Nearest Neighbors and assigns a class label based on majority voting of its k-nearest neighbors.
K Means is a clustering algorithm that partitions data into k clusters based on similarity.
KNN requires labeled data for training, while K Means does not need labeled data.
KNN ...
- Q4. Range of Cross Entropy Loss
- Ans.
Cross entropy loss measures the difference between two probability distributions.
Range of cross entropy loss is [0, infinity)
Lower values indicate better model performance
Commonly used in classification tasks
Skills evaluated in this interview
(2 Questions)
- Q1. Introduced yourself.?
- Q2. What is linear and logistics.?
- Ans.
Linear regression is a statistical method to model the relationship between a dependent variable and one or more independent variables. Logistic regression is used to model the probability of a binary outcome.
Linear regression is used for predicting continuous outcomes, while logistic regression is used for predicting binary outcomes.
In linear regression, the relationship between the independent and dependent variables...
Data Science Project
Interview Preparation Tips
(3 Questions)
- Q1. What is linear regression and logistics regression?
- Ans.
Linear regression is a statistical method to model the relationship between a dependent variable and one or more independent variables. Logistic regression is used to model the probability of a binary outcome.
Linear regression is used for predicting continuous outcomes, while logistic regression is used for predicting binary outcomes.
Linear regression assumes a linear relationship between the independent and dependent ...
- Q2. What is central limit theorem? Why we use it
- Ans.
Central Limit Theorem states that the sampling distribution of the sample mean approaches a normal distribution as the sample size increases.
Central Limit Theorem is used to make inferences about a population mean based on the sample mean.
It allows us to use the properties of the normal distribution to estimate population parameters.
It is essential in hypothesis testing and constructing confidence intervals.
For example...
- Q3. What is support vector machine?
- Ans.
Support Vector Machine is a supervised machine learning algorithm used for classification and regression tasks.
Support Vector Machine finds the hyperplane that best separates different classes in the feature space
It works by maximizing the margin between the hyperplane and the nearest data points, known as support vectors
SVM can handle both linear and non-linear data by using different kernel functions like linear, pol
Skills evaluated in this interview
Feynn Labs interview questions for designations
(1 Question)
- Q1. What are supervied learning
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Selected
I applied via Campus Placement and was interviewed in Nov 2024. There were 3 interview rounds.
There were verbal, non verbal, reasoning , English and maths questions
(2 Questions)
- Q1. Tell me about your project.
- Ans.
I worked on a project analyzing customer behavior using machine learning algorithms.
Used Python for data preprocessing and analysis
Implemented machine learning models such as decision trees and logistic regression
Performed feature engineering to improve model performance
- Q2. What programming knowledge you have ?
- Ans.
Proficient in Python, R, and SQL with experience in data manipulation, visualization, and machine learning algorithms.
Proficient in Python for data analysis and machine learning tasks
Experience with R for statistical analysis and visualization
Knowledge of SQL for querying databases and extracting data
Familiarity with libraries such as Pandas, NumPy, Matplotlib, and Scikit-learn
(2 Questions)
- Q1. Where do you stay ?
- Ans.
I currently stay in an apartment in downtown area.
I stay in an apartment in downtown area
My current residence is in a city
I live close to my workplace
- Q2. Tell me about you
- Ans.
I am a data science enthusiast with a strong background in statistics and machine learning.
Background in statistics and machine learning
Passionate about data science
Experience with data analysis tools like Python and R
I applied via Campus Placement
(1 Question)
- Q1. ML and deep learning questions
(2 Questions)
- Q1. Projects discussion
- Q2. Chatgpt architecture
Basic DSA questions will be asked Leetcode Easy to medium
(2 Questions)
- Q1. BERT vs LSTM and their speed
- Ans.
BERT is faster than LSTM due to its transformer architecture and parallel processing capabilities.
BERT utilizes transformer architecture which allows for parallel processing of words in a sentence, making it faster than LSTM which processes words sequentially.
BERT has been shown to outperform LSTM in various natural language processing tasks due to its ability to capture long-range dependencies more effectively.
For exa...
- Q2. What is multinomial Naive Bayes theorem
- Ans.
Multinomial Naive Bayes is a classification algorithm based on Bayes' theorem with the assumption of independence between features.
It is commonly used in text classification tasks, such as spam detection or sentiment analysis.
It is suitable for features that represent counts or frequencies, like word counts in text data.
It calculates the probability of each class given the input features and selects the class with the
Skills evaluated in this interview
Selected
I applied via campus placement at Chennai Mathematical Institute, Chennai and was interviewed in Dec 2023. There was 1 interview round.
(3 Questions)
- Q1. What are Large Language Models?
- Ans.
Large Language Models are advanced AI models that can generate human-like text based on input data.
Large Language Models use deep learning techniques to understand and generate text.
Examples include GPT-3 (Generative Pre-trained Transformer 3) and BERT (Bidirectional Encoder Representations from Transformers).
They are trained on vast amounts of text data to improve their language generation capabilities.
- Q2. Do you know about RAGs?
- Ans.
RAGs stands for Red, Amber, Green. It is a project management tool used to visually indicate the status of tasks or projects.
RAGs is commonly used in project management to quickly communicate the status of tasks or projects.
Red typically indicates tasks or projects that are behind schedule or at risk.
Amber signifies tasks or projects that are on track but may require attention.
Green represents tasks or projects that ar...
- Q3. Which is the best clustering algorithm?
- Ans.
There is no one-size-fits-all answer as the best clustering algorithm depends on the specific dataset and goals.
The best clustering algorithm depends on the dataset characteristics such as size, dimensionality, and noise level.
K-means is popular for its simplicity and efficiency, but may not perform well on non-linear data.
DBSCAN is good for clusters of varying shapes and sizes, but may struggle with high-dimensional d...
Skills evaluated in this interview
Selected
I applied via Campus Placement and was interviewed in Jun 2024. There were 2 interview rounds.
Some basic aptitude questions were asked , but had to be solved in 20 minutes
Medium level 2 leet code questions were asked and i cleared both
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Feynn Labs Data Science Intern Interview Process
based on 7 interviews
1 Interview rounds
- Technical Round
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