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I applied via Referral and was interviewed before Dec 2021. There was 1 interview round.
Linear Regression predicts continuous values while Logistic Regression predicts binary outcomes.
Linear Regression is used for predicting continuous values while Logistic Regression is used for predicting binary outcomes.
Linear Regression uses a linear approach to model the relationship between dependent and independent variables while Logistic Regression uses a logistic function to model the probability of a binary out...
I applied via LinkedIn and was interviewed in Mar 2021. There was 1 interview round.
R2 and adj R2 are statistical measures used to evaluate the goodness of fit of a regression model.
R2 measures the proportion of variance in the dependent variable that is explained by the independent variable(s).
Adjusted R2 is a modified version of R2 that takes into account the number of independent variables in the model.
R2 ranges from 0 to 1, with higher values indicating a better fit.
Adjusted R2 can be negative if ...
I rate myself 8/10 in Python. I have experience in data manipulation, visualization, and machine learning.
Proficient in Python libraries such as Pandas, NumPy, Matplotlib, and Scikit-learn
Experience in data cleaning, preprocessing, and feature engineering
Developed machine learning models for classification, regression, and clustering
Familiar with deep learning frameworks such as TensorFlow and Keras
Implemented neural n...
Logit regression is a statistical method used to model binary outcomes.
Logit regression is used when the dependent variable is binary (0 or 1).
It models the probability of the dependent variable taking the value 1.
It uses the logistic function to transform the linear regression equation into a probability.
It is a type of generalized linear model (GLM).
Validation of a model involves testing its performance on new data to ensure its accuracy and generalizability.
Split data into training and testing sets
Train model on training set
Test model on testing set
Evaluate model performance using metrics such as accuracy, precision, recall, and F1 score
Repeat process with different validation techniques such as cross-validation or bootstrapping
Model optimization is the process of improving the performance of a machine learning model by adjusting its parameters.
Model optimization involves finding the best set of hyperparameters for a given model.
It can be done using techniques like grid search, random search, and Bayesian optimization.
The goal is to improve the model's accuracy, precision, recall, or other performance metrics.
Model optimization is an iterativ...
NER training using deep learning
I approach assignments by breaking them down into smaller tasks, setting deadlines, and regularly checking progress.
Break down the assignment into smaller tasks to make it more manageable
Set deadlines for each task to stay on track
Regularly check progress to ensure everything is on schedule
Seek feedback from colleagues or supervisors to improve the quality of work
I applied via Job Fair and was interviewed in May 2024. There were 3 interview rounds.
They gave a span of 3 days to build an AI-powered webapp
I have experience working with cloud technologies such as AWS, Azure, and Google Cloud Platform.
Experience in setting up and managing virtual machines, storage, and networking in cloud environments
Knowledge of cloud services like EC2, S3, RDS, and Lambda
Experience with cloud-based data processing and analytics tools like AWS Glue and Google BigQuery
Developed a predictive model for customer churn in a telecom company
Collected and cleaned customer data from various sources
Performed exploratory data analysis to identify key factors influencing churn
Built and fine-tuned machine learning models to predict customer churn
Challenges included imbalanced data, feature engineering, and model interpretability
I was interviewed in May 2024.
Maths and stats refer to the study of mathematical concepts and statistical methods for analyzing data.
Maths involves the study of numbers, quantities, shapes, and patterns.
Stats involves collecting, analyzing, interpreting, and presenting data.
Maths is used to solve equations, calculate probabilities, and model real-world phenomena.
Stats is used to make informed decisions, draw conclusions, and test hypotheses.
Both ma...
Confusion matrix what are your job rolls explain me Gradient boosting algorithm?
posted on 18 Jan 2025
I was interviewed in Dec 2024.
Asked the question about ml and basic python questions
I applied via Campus Placement
Bulb and switch puzzle
Rope burning and length question
I applied via Naukri.com and was interviewed in Apr 2023. There were 3 interview rounds.
Finding index of 2 numbers having total equal to target in a list without nested for loop.
Use dictionary to store the difference between target and each element of list.
Iterate through list and check if element is in dictionary.
Return the indices of the two elements that add up to target.
Random forest and KNN are machine learning algorithms used for classification and regression tasks.
Random forest is an ensemble learning method that constructs multiple decision trees and combines their outputs to make a final prediction.
KNN (k-nearest neighbors) is a non-parametric algorithm that classifies new data points based on the majority class of their k-nearest neighbors in the training set.
Random forest is us...
To find unique keys in 2 dictionaries.
Create a set of keys for each dictionary
Use set operations to find the unique keys
Return the unique keys
AWS EC2 model deployment involves creating an instance, installing necessary software, and deploying the model.
Create an EC2 instance with the desired specifications
Install necessary software and dependencies on the instance
Upload the model and any required data to the instance
Deploy the model using a web server or API
Monitor the instance and model performance for optimization
Overloading is the ability to define multiple methods with the same name but different parameters.
Overloading allows for more flexibility in method naming and improves code readability.
Examples include defining multiple constructors for a class with different parameter lists or defining a method that can accept different data types as input.
Overloading is resolved at compile-time based on the number and types of argume...
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