Feynn Labs Interview Questions, Process, and Tips

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...read more
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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...read more
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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 ...read more
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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
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Q1. Some basic machine learning algorithms related
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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...read more
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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...read more
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Q1. Introduced yourself.?
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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...read more
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Data Science Project

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...read more
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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
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Write the code for logistic Regression

Q1. All question on machines learning concept
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Q2. Concepts on backward propagation
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