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I applied via Recruitment Consulltant and was interviewed in Dec 2024. There were 2 interview rounds.
Chunking methods are used in natural language processing to group words into meaningful chunks.
Regular expression based chunking: Uses regular expressions to define patterns for chunking.
Rule-based chunking: Utilizes predefined rules to identify and group chunks.
Statistical chunking: Involves using statistical models to automatically identify chunks.
Shallow parsing: Identifies and groups chunks based on part-of-speech ...
Optimizing the RAG Pipeline involves improving efficiency and accuracy of the pipeline for better performance.
Optimize hyperparameters of the models used in the pipeline
Implement feature engineering techniques to improve model performance
Use efficient algorithms for processing data
Parallelize tasks to reduce processing time
Regularly monitor and update the pipeline for continuous improvement
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I applied via Company Website and was interviewed in Nov 2024. There were 2 interview rounds.
Logical, Verbal, reasoning 90 mins
I applied via Naukri.com and was interviewed in Aug 2024. There were 2 interview rounds.
Evaluation metrics for classification are used to assess the performance of a classification model.
Common evaluation metrics include accuracy, precision, recall, F1 score, and ROC-AUC.
Accuracy measures the proportion of correctly classified instances out of the total instances.
Precision measures the proportion of true positive predictions out of all positive predictions.
Recall measures the proportion of true positive p...
L1 and L2 regression are regularization techniques used in machine learning to prevent overfitting.
L1 regression adds a penalty equivalent to the absolute value of the magnitude of coefficients.
L2 regression adds a penalty equivalent to the square of the magnitude of coefficients.
L1 regularization can lead to sparse models, while L2 regularization tends to shrink coefficients towards zero.
L1 regularization is also know...
Random forest is an ensemble learning algorithm that builds multiple decision trees and combines their predictions.
Random forest creates multiple decision trees using bootstrapping and feature randomization.
Each tree in the random forest is trained on a subset of the data and features.
The final prediction is made by averaging the predictions of all the trees (regression) or taking a majority vote (classification).
I am a dedicated and passionate Machine Learning Engineer with a strong background in computer science and data analysis.
Experienced in developing machine learning models for various applications
Proficient in programming languages such as Python, R, and Java
Skilled in data preprocessing, feature engineering, and model evaluation
Strong understanding of algorithms and statistical concepts
Excellent problem-solving and ana
I applied via Referral and was interviewed in Sep 2024. There was 1 interview round.
posted on 24 Jul 2024
Bias is error due to overly simplistic assumptions, variance is error due to overly complex models.
Bias is error introduced by approximating a real-world problem, leading to underfitting.
Variance is error introduced by modeling the noise in the training data, leading to overfitting.
High bias can cause a model to miss relevant relationships between features and target variable.
High variance can cause a model to be overl...
Learning rate is a hyperparameter that controls how much we are adjusting the weights of our network with respect to the loss gradient.
Learning rate determines the size of the steps taken during optimization.
A high learning rate can cause the model to converge too quickly and potentially miss the optimal solution.
A low learning rate can cause the model to take a long time to converge or get stuck in a local minimum.
Com...
I applied via Walk-in and was interviewed in Sep 2023. There were 4 interview rounds.
Problem solving skills
Logical reasoning numerical reasoning abstract reasoning verbal reasoning
posted on 26 May 2024
I applied via Approached by Company and was interviewed before May 2023. There were 2 interview rounds.
I applied via Recruitment Consulltant and was interviewed in Jan 2022. There were 2 interview rounds.
I applied via LinkedIn and was interviewed in Jun 2024. There were 4 interview rounds.
Machine learning - Code K-Means
Machine Learning - Code Neural Network
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Software Engineering Senior Analyst
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Software Engineering Advisor
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TCS
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