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Standard interview qns. Asking about yourself, experience and where do you see yourself in 5 years
I applied via Naukri.com and was interviewed in Oct 2020. There were 3 interview rounds.
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I applied via Naukri.com and was interviewed in Nov 2023. There were 2 interview rounds.
I applied via Naukri.com and was interviewed before Sep 2022. There were 3 interview rounds.
I appeared for an interview before Oct 2021.
I applied via Campus Placement and was interviewed in Jul 2020. There were 4 interview rounds.
I will assess the client's current infrastructure, identify areas of improvement, and propose a customized upgrade plan.
Conduct a thorough assessment of the client's current infrastructure
Identify areas of improvement and prioritize them based on their impact on the client's business
Propose a customized upgrade plan that addresses the client's specific needs and budget
Ensure minimal disruption to the client's operation...
I applied via Naukri.com and was interviewed before Nov 2023. There were 2 interview rounds.
Create a business requirement document
Strengths include strong analytical skills and attention to detail. Weaknesses may include difficulty with public speaking and time management.
Strengths: strong analytical skills
Strengths: attention to detail
Weaknesses: difficulty with public speaking
Weaknesses: time management
I applied via Campus Placement and was interviewed before Sep 2023. There was 1 interview round.
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 type of ensemble learning method.
It builds multiple decision trees during training.
Each tree is built using a subset of the training data and a random subset of features.
The final prediction is made by averaging the predictions of all the individual trees.
Random...
Boosting is a machine learning ensemble technique where multiple weak learners are combined to create a strong learner.
Boosting is an iterative process where each weak learner is trained based on the errors of the previous learners.
Examples of boosting algorithms include AdaBoost, Gradient Boosting, and XGBoost.
Boosting is used to improve the accuracy of models and reduce bias and variance.
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