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Feynn Labs Machine Learning Engineer Intern Interview Questions, Process, and Tips
Top Feynn Labs Machine Learning Engineer Intern Interview Questions and Answers
- Q1. What is bagging and boosting. What are different types of learning models. Explain Tree based models.
- Q2. what is difference between Logistic and Linear Regression
- Q3. What is KNN and K-means
Feynn Labs Machine Learning Engineer Intern Interview Experiences
2 interviews found
Machine Learning Engineer Intern Interview Questions & Answers
SAHIL CHOURASIYA
posted on 14 Jul 2024
(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...
- 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
Write the code for logistic Regression
Interview Preparation Tips
Skills evaluated in this interview
Selected
I applied via Internshala and was interviewed before Aug 2022. There were 2 interview rounds.
(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...
- 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...
- 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 ...
- 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
- 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...
Interview Preparation Tips
Skills evaluated in this interview
Top trending discussions
Interview questions from similar companies
(2 Questions)
- Q1. Details explain about project
- Q2. Explain Module in ml
- Ans.
A module in machine learning is a self-contained unit that performs a specific task or function.
Modules can include algorithms, data preprocessing techniques, evaluation metrics, etc.
Modules can be combined to create a machine learning pipeline.
Examples of modules include decision trees, support vector machines, and k-means clustering.
Skills evaluated in this interview
Manasi Patil
I applied via Company Website and was interviewed in Nov 2024. There were 2 interview rounds.
Logical, Verbal, reasoning 90 mins
(2 Questions)
- Q1. ML algorithms and Explain it?
- Q2. Print even numbers in for loop?
Shirish Mane
I applied via Naukri.com and was interviewed in Aug 2024. There were 2 interview rounds.
(3 Questions)
- Q1. What is evaluation Matrix for classification
- Ans.
Evaluation metrics for classification are used to assess the performance of a classification model.
Common evaluation metrics include accuracy, precision, recall, F1 score, and ROC-AUC.
Accuracy measures the proportion of correctly classified instances out of the total instances.
Precision measures the proportion of true positive predictions out of all positive predictions.
Recall measures the proportion of true positive p...
- Q2. What L1 and L2 regression
- Ans.
L1 and L2 regression are regularization techniques used in machine learning to prevent overfitting.
L1 regression adds a penalty equivalent to the absolute value of the magnitude of coefficients.
L2 regression adds a penalty equivalent to the square of the magnitude of coefficients.
L1 regularization can lead to sparse models, while L2 regularization tends to shrink coefficients towards zero.
L1 regularization is also know...
- Q3. Explain random forest algorithm
- Ans.
Random forest is an ensemble learning algorithm that builds multiple decision trees and combines their predictions.
Random forest creates multiple decision trees using bootstrapping and feature randomization.
Each tree in the random forest is trained on a subset of the data and features.
The final prediction is made by averaging the predictions of all the trees (regression) or taking a majority vote (classification).
(2 Questions)
- Q1. Tell me about self
- Ans.
I am a dedicated and passionate Machine Learning Engineer with a strong background in computer science and data analysis.
Experienced in developing machine learning models for various applications
Proficient in programming languages such as Python, R, and Java
Skilled in data preprocessing, feature engineering, and model evaluation
Strong understanding of algorithms and statistical concepts
Excellent problem-solving and ana
- Q2. Questions about salary discuss
Skills evaluated in this interview
Selected
I was interviewed in Aug 2024.
The test will evaluate your proficiency in English and will include some basic data interpretation tasks.
(2 Questions)
- Q1. Do you have any experience as a team leader?
- Q2. Can you provide your introduction?
Manager Learning & Development Interview Questions & Answers
Accenture
Anonymous
posted on 16 May 2024
Selected
I applied via Recruitment Consulltant and was interviewed in Apr 2024. There were 3 interview rounds.
(2 Questions)
- Q1. Screening round: Tell me about your experiences?
- Q2. Tell me Learning and development challenges you faced and improvements you have done
Genral and technical aptitude test
(5 Questions)
- Q1. Overall experience?
- Q2. What was challenging role in previous org?
- Q3. How you see L&D from your perspective?
- Q4. How you tackle problems in your role?
- Q5. How will you onboard 500 candidates every month?
- Ans.
By creating a structured onboarding process, utilizing technology for efficiency, and leveraging a team of trainers.
Develop a comprehensive onboarding program with clear objectives and timelines.
Utilize technology such as online training modules and virtual onboarding sessions.
Assign a team of trainers to handle different aspects of the onboarding process.
Implement a buddy system where existing employees mentor new hir...
Interview Preparation Tips
I applied via Referral and was interviewed in Sep 2024. There was 1 interview round.
(2 Questions)
- Q1. Basic Stats questions
- Q2. Basic ML questions
Learning & Development Specialist Interview Questions & Answers
BYJU'S
Anonymous
posted on 19 Sep 2024
Selected
I applied via LinkedIn
(2 Questions)
- Q1. Describe about yourself
- Ans.
I am a passionate and experienced Learning & Development Specialist with a strong background in designing and delivering effective training programs.
Over 5 years of experience in creating engaging learning materials
Skilled in conducting needs assessments and developing training plans
Proficient in utilizing various instructional design methodologies
Strong communication and presentation skills
Proven track record of impro...
- Q2. Why is this job
Selected
(1 Question)
- Q1. Started with basic os or network fundamentals like analyzing a core dump, handling tcp packet loss, difference between rest api and grpc etc. Then, from ML started with basic questions like bias variance t...
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Feynn Labs Machine Learning Engineer Intern Interview Process
based on 2 interviews
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Feynn Labs Machine Learning Engineer Intern Reviews and Ratings
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50
salaries
|  ₹0.5 L/yr - ₹5 L/yr |
|
Intern
7
salaries
| ₹1 L/yr - ₹4 L/yr |
|
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5
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| ₹1 L/yr - ₹1.5 L/yr |
|
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4
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| ₹1 L/yr - ₹5 L/yr |
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