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Feynn Labs Interview Questions and Answers
46 Interview questions
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
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 pa...
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...
KNN algorithm is a simple, instance-based learning algorithm used for classification and regression tasks.
KNN stands for K-Nearest Neighbors.
It classifies a new data point based on majority class of its k nearest neighbors.
KNN is a lazy learning algorithm as it does not learn a discriminative function from the training data.
It is sensitive to the choice of k value and distance metric.
Example: Classifying a flower ...
Linear regression is used for continuous variables, while logistic regression is used for binary classification.
Linear regression predicts continuous values, while logistic regression predicts probabilities between 0 and 1.
Linear regression uses a linear equation to model the relationship between the independent and dependent variables.
Logistic regression uses the logistic function to model the probability of a bi...
Max pooling reduces dimensionality by selecting the maximum value from a feature map segment.
Max pooling is commonly used in Convolutional Neural Networks (CNNs).
It helps to down-sample feature maps, reducing computational load.
For example, a 2x2 max pooling operation selects the highest value from each 2x2 block.
It retains important features while discarding less significant information.
Max pooling can help in ac...
Max pooling is a down-sampling technique in deep learning where the maximum value from a set of values is selected.
Max pooling reduces the spatial dimensions of the input data by selecting the maximum value from a set of values in a specific window.
It helps in reducing the computational complexity and controlling overfitting in the model.
Example: In a 2x2 max pooling operation, the maximum value from each 2x2 wind...
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...
Feynn Labs Interview Experiences
30 interviews found
Anonymous
I applied via Naukri.com and was interviewed in Sep 2024. There was 1 interview round.
(3 Questions)
- Q1. What is svm,how many dimensions in rbf?
- Ans.
SVM stands for Support Vector Machine, RBF stands for Radial Basis Function. RBF can have infinite dimensions.
SVM is a supervised machine learning algorithm used for classification and regression tasks.
RBF is a kernel function used in SVM to map data into a higher-dimensional space.
RBF can have infinite dimensions, allowing it to capture complex relationships in the data.
- Q2. Different 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), while linear regression predicts a continuous outcome.
Logistic regression uses a sigmoid function to map predicted values between 0 and 1, while linear regression uses a linear function.
Logistic regression is more suitable for classificatio...
- Q3. What is random partition
- Ans.
Random partition is a method of dividing a dataset into random subsets for training and testing purposes.
Random partition helps in evaluating the performance of a machine learning model by training it on one subset and testing it on another.
It helps in preventing overfitting by ensuring that the model is tested on unseen data.
Random partition is commonly used in techniques like k-fold cross-validation where the dataset...
Interview Preparation Tips
Skills evaluated in this interview
Selected
I applied via Naukri.com and was interviewed in Aug 2024. There was 1 interview round.
(2 Questions)
- Q1. What is logistic regression
- Ans.
Logistic regression is a statistical model used to predict the probability of a binary outcome based on one or more predictor variables.
Logistic regression is used when the dependent variable is binary (e.g., 0 or 1, yes or no).
It estimates the probability that a given observation belongs to a particular category.
The output of logistic regression is a probability score between 0 and 1.
It uses the logistic function (sig...
- Q2. How can you use K - Means?
- Ans.
K-Means is a clustering algorithm used to group data points into K clusters based on similarity.
Choose the number of clusters (K) you want to create
Randomly initialize K cluster centroids
Assign each data point to the nearest centroid
Update the centroids based on the mean of the data points assigned to each cluster
Repeat the assignment and update steps until convergence
Skills evaluated in this interview
Selected
I applied via Naukri.com and was interviewed in Jul 2024. There was 1 interview round.
(2 Questions)
- Q1. What types of machine learning project you worked on?
- Ans.
I have worked on projects involving image classification, natural language processing, and predictive modeling.
Image classification using convolutional neural networks
Sentiment analysis using recurrent neural networks
Predictive modeling for sales forecasting
- Q2. What is difference between logistics and linear regression?
- Ans.
Logistic regression is used for binary classification while linear regression is used for regression tasks.
Logistic regression is used when the dependent variable is binary (0 or 1), while linear regression is used when the dependent variable is continuous.
Logistic regression predicts the probability of a certain class or event occurring, while linear regression predicts a continuous value.
Logistic regression uses a si...
Skills evaluated in this interview
Selected
I appeared for an interview in Jul 2024.
(2 Questions)
- Q1. What is random forest? What it is called random?
- Ans.
Random forest is an ensemble learning method used for classification and regression tasks, consisting of multiple decision trees.
Random forest is made up of multiple decision trees, where each tree is built using a subset of the training data and a random subset of features.
During prediction, each tree in the forest independently predicts the output, and the final output is determined by a majority vote (classification...
- Q2. What is svm? Any project you perform using this?
- Ans.
SVM stands for Support Vector Machine, a supervised machine learning algorithm used for classification and regression tasks.
SVM finds the hyperplane that best separates different classes in the feature space.
It can handle both linear and non-linear data by using different kernel functions.
Example project: Sentiment analysis using SVM to classify movie reviews as positive or negative.
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
Selected
I applied via Naukri.com and was interviewed in Jul 2024. There were 2 interview rounds.
(1 Question)
- Q1. Some basic machine learning algorithms related
Eeva Eldhose
Selected
I appeared for an interview in Feb 2025, where I was asked the following questions.
- Q1. What is supervised learning
- Ans.
Supervised learning is a type of machine learning where a model is trained on labeled data to make predictions or classifications.
Involves training a model on a dataset with input-output pairs.
Common algorithms include linear regression, decision trees, and support vector machines.
Used for tasks like classification (e.g., spam detection) and regression (e.g., predicting house prices).
The model learns to map inputs to o...
- Q2. What are the parameters of machine learning
- Ans.
Machine learning parameters include hyperparameters, model parameters, and training parameters that influence model performance.
Hyperparameters: Settings that are not learned from the data, e.g., learning rate, batch size.
Model Parameters: Weights and biases learned during training, e.g., coefficients in linear regression.
Training Parameters: Settings related to the training process, e.g., number of epochs, optimizatio...
(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
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 Company Website and was interviewed in May 2024. There was 1 interview round.
(2 Questions)
- Q1. All question on machines learning concept
- Q2. Concepts on backward propagation
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