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Feynn Labs Machine Learning Engineer Intern Interview Questions, Process, and Tips

Updated 14 Jul 2024

Top Feynn Labs Machine Learning Engineer Intern Interview Questions and Answers

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Feynn Labs Machine Learning Engineer Intern Interview Experiences

2 interviews found

Interview experience
5
Excellent
Difficulty level
-
Process Duration
-
Result
-
Round 1 - Technical 

(2 Questions)

  • Q1. What is 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 input features.

    • Linear regression predicts a continuous value based on input features.

    • Logistic regression uses a sigmoid function to map predicted values between 0 and 1.

    • Linear regression uses a linear equation to model the relations...

  • Answered by AI
  • Q2. What are the loss functions
  • Ans. 

    Loss functions are used to measure the difference between predicted values and actual values in machine learning models.

    • Loss functions quantify how well a model is performing by comparing predicted values to actual values

    • Common loss functions include Mean Squared Error (MSE), Cross Entropy Loss, and Hinge Loss

    • Different loss functions are used for different types of machine learning tasks, such as regression or classifi

  • Answered by AI
Round 2 - Coding Test 

Write the code for logistic Regression

Interview Preparation Tips

Interview preparation tips for other job seekers - Prepare well for the machine Learning concepts

Skills evaluated in this interview

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Interview experience
5
Excellent
Difficulty level
Moderate
Process Duration
Less than 2 weeks
Result
Selected Selected

I applied via Internshala and was interviewed before Aug 2022. There were 2 interview rounds.

Round 1 - Resume Shortlist 
Pro Tip by AmbitionBox:
Keep your resume crisp and to the point. A recruiter looks at your resume for an average of 6 seconds, make sure to leave the best impression.
View all Resume tips
Round 2 - One-on-one 

(5 Questions)

  • Q1. What is bagging and boosting. What are different types of learning models. Explain Tree based models.
  • Ans. 

    Bagging and boosting are ensemble learning techniques. Tree based models are decision trees used for classification and regression.

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

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

    • Different types of learning models in...

  • Answered by AI
  • Q2. What is KNN and K-means
  • Ans. 

    KNN is a supervised machine learning algorithm used for classification and regression. K-means is an unsupervised clustering algorithm.

    • KNN stands for K-Nearest Neighbors and works by finding the K closest data points to a given data point to make predictions.

    • K-means is a clustering algorithm that partitions data into K clusters based on similarity.

    • KNN is used for classification tasks, while K-means is used for clusteri...

  • Answered by AI
  • Q3. What is supervised learning.
  • Ans. 

    Supervised 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 to make predictions or decisions.

    • It involves mapping input data to the correct output label based on the input-output pairs provided during training.

    • Common examples include classification and regression tasks, such as predicting whether an email is spam or ...

  • Answered by AI
  • Q4. What is unsupervised learning.
  • Ans. 

    Unsupervised learning is a type of machine learning where the model learns patterns from unlabeled data.

    • No explicit labels are provided in unsupervised learning

    • The model must find patterns and relationships in the data on its own

    • Clustering and dimensionality reduction are common techniques in unsupervised learning

  • Answered by AI
  • Q5. What is random forest.
  • Ans. 

    Random forest is an ensemble learning method that builds multiple decision trees and merges them to improve accuracy and prevent overfitting.

    • Random forest is a collection of decision trees that are trained on random subsets of the data.

    • Each tree in the random forest independently predicts the target variable, and the final prediction is made by averaging the predictions of all trees.

    • Random forest is effective in handli...

  • Answered by AI

Interview Preparation Tips

Interview preparation tips for other job seekers - Be prepared theortically in machine learning.

Skills evaluated in this interview

Machine Learning Engineer Intern Interview Questions Asked at Other Companies

Q1. How can we write an efficient matrix multiplication method for hu ... read more
asked in Feynn Labs
Q2. What is bagging and boosting. What are different types of learnin ... read more
asked in NCR Atleos
Q3. Tell be about Supervised and Unsupervised Machine learning
Q4. How can we further optimize this?
asked in Feynn Labs
Q5. what is difference between Logistic and Linear Regression

Interview questions from similar companies

Interview experience
5
Excellent
Difficulty level
Easy
Process Duration
Less than 2 weeks
Result
Selected Selected

I applied via Approached by Company and was interviewed in Dec 2023. There was 1 interview round.

Round 1 - HR 

(3 Questions)

  • Q1. How many working days
  • Q2. Company give another benefit
  • Q3. Work load is high or low
Interview experience
5
Excellent
Difficulty level
Moderate
Process Duration
Less than 2 weeks
Result
Selected Selected

I applied via Campus Placement and was interviewed in Jun 2024. There were 2 interview rounds.

Round 1 - Aptitude Test 

Some basic aptitude questions were asked , but had to be solved in 20 minutes

Round 2 - Coding Test 

Medium level 2 leet code questions were asked and i cleared both

Interview experience
5
Excellent
Difficulty level
Easy
Process Duration
Less than 2 weeks
Result
Selected Selected

I applied via Campus Placement and was interviewed in Dec 2023. There was 1 interview round.

Round 1 - One-on-one 

(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.

  • Answered by AI
  • 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...

  • Answered by AI
  • 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...

  • Answered by AI

Skills evaluated in this interview

Interview experience
5
Excellent
Difficulty level
-
Process Duration
-
Result
Selected Selected

I was interviewed before Mar 2023.

Round 1 - Technical 

(1 Question)

  • Q1. Oops concepts, access specifiers, run time and compile time polymorphism, type casting, inorder tree traversal, how to identify loop using linked list and identify the first node of a loop, copy constructo...
Round 2 - One-on-one 

(1 Question)

  • Q1. This was a technical round with the manager. Questions were asked on the work done in the previous organization
Round 3 - HR 

(1 Question)

  • Q1. Casual discussion

I applied via Campus Placement and was interviewed before Feb 2020. There were 4 interview rounds.

Interview Questionnaire 

1 Question

  • Q1. Hr

Interview Preparation Tips

Interview preparation tips for other job seekers - You should be true to what you are putting before the interviewer . Try to put your ideas Add something you did well in your career like in projects /research which you know very well and versed in concepts about it for open interview so that interviewer can get bandwidth where he can ask questions from. This is simply a key .

I applied via Campus Placement and was interviewed in Dec 2020. There was 1 interview round.

Interview Questionnaire 

1 Question

  • Q1. 1.Describe a situation where you have taken a quick decision and failed, and a situation where you succeeded. 2. What is a skill you have tried to achieve but failed? 3. Describe a decision you have taken ...

Interview Preparation Tips

Interview preparation tips for other job seekers - 1. Always try to take risk in small problems . When you face the consequences, you would be able to tackle bigger problems.
2. Every skill you learn, even if you didn't excel in it is not to be considered a waste of time/failure, you learn something simply by participating.

I applied via Campus Placement and was interviewed before Jun 2020. There were 3 interview rounds.

Interview Questionnaire 

1 Question

  • Q1. Tell me about Yourself, some questions related to machine learning, and I was asked more questions to differentiate like differences between ml and ai, python and c, c and java and procedural and functiona...

Interview Preparation Tips

Interview preparation tips for other job seekers - Get a complete grip on your resume and be confident about what you say, If you don't know the answer it is okay to agree that you don't know the answer so that interviewer can ask the next questions.

I was interviewed before Jun 2021.

Round 1 - Coding Test 

(2 Questions)

Round duration - 180 minutes
Round difficulty - Easy

It was an mcq + coding round. There were aptitude and ouput based question in mcq. And coding questions were easy

  • Q1. 

    Find the Duplicate Number Problem Statement

    Given an integer array 'ARR' of size 'N' containing numbers from 0 to (N - 2). Each number appears at least once, and there is one number that appears twice. Yo...

  • Ans. 

    Find the duplicate number in an array of integers from 0 to (N-2).

    • Iterate through the array and keep track of the frequency of each number using a hashmap.

    • Return the number with a frequency greater than 1 as the duplicate number.

    • Time complexity can be optimized to O(N) using Floyd's Tortoise and Hare algorithm.

  • Answered by AI
  • Q2. 

    Reverse String Operations Problem Statement

    You are provided with a string S and an array of integers A of size M. Your task is to perform M operations on the string as specified by the indices in array A...

  • Ans. 

    Given a string and an array of indices, reverse substrings based on the indices to obtain the final string.

    • Iterate through the array of indices and reverse the substrings accordingly

    • Ensure the range specified by each index is non-empty

    • Return the final string after all operations are completed

  • Answered by AI
Round 2 - Video Call 

Round duration - 60 Minutes
Round difficulty - Easy

It was technical + hr round. there were 2 people as interviewer. They stated from intro and asked some basic puzzles and hr questions. After that they asked about my projects, technologies and some ds algo and dbms questions.

Interview Preparation Tips

Eligibility criterianaAccenture interview preparation:Topics to prepare for the interview - Data Structures, Pointers, OOPS, System Design, Algorithms, Dynamic ProgrammingTime required to prepare for the interview - 6 MonthsInterview preparation tips for other job seekers

Tip 1 : Practice aptitude
Tip 2 : Focus on practicing coding
Tip 3 : Learn from mistakes

Application resume tips for other job seekers

Tip 1 : Mention some projects that you have done
Tip 2 : Try to have skills that are required for the role

Final outcome of the interviewSelected

Skills evaluated in this interview

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Feynn Labs Interview FAQs

How many rounds are there in Feynn Labs Machine Learning Engineer Intern interview?
Feynn Labs interview process usually has 2 rounds. The most common rounds in the Feynn Labs interview process are Resume Shortlist, One-on-one Round and Technical.
What are the top questions asked in Feynn Labs Machine Learning Engineer Intern interview?

Some of the top questions asked at the Feynn Labs Machine Learning Engineer Intern interview -

  1. What is bagging and boosting. What are different types of learning models. Expl...read more
  2. what is difference between Logistic and Linear Regress...read more
  3. What is KNN and K-me...read more

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Feynn Labs Machine Learning Engineer Intern Interview Process

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3.2

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3.9

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