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Feynn Labs Machine Learning Engineer Intern Interview Questions and Answers
7 Interview questions
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 cla...
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 rela...
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 h...
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
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 spa...
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 mode...
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 clu...
Feynn Labs Machine Learning Engineer Intern Interview Experiences
3 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
Anonymous
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
Selected
I appeared for an interview before Apr 2024, where I was asked the following questions.
- Q1. Job description
- Q2. Knowledge of ML
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- 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 appeared for an interview in Dec 2023.
English, number system, grammar
Python , data science, machine learning
Python machine learning, natural language precossing
(4 Questions)
- Q1. Python , Data Science, SQL
- Q2. What is python basics, libraries
- Ans.
Python basics include syntax, data types, and control structures. Libraries like NumPy, Pandas, and Matplotlib enhance data analysis and visualization.
Python basics cover syntax, variables, data types, and control structures.
NumPy is a library for numerical computing, providing powerful array operations.
Pandas is a library for data manipulation and analysis, offering data structures like DataFrames.
Matplotlib is a libr...
- Q3. Data science algorithms , and theory
- Q4. Sql querys, cluases
Indian environment, village, college days
(1 Question)
- Q1. Python, data science
Selected
I applied via Campus Placement 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
Arbaz Ali
Selected
I applied via Approached by Company and was interviewed in Dec 2023. There was 1 interview round.
(3 Questions)
- Q1. How many working days
- Q2. Company give another benefit
- Q3. Work load is high or low
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