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I applied via Approached by Company and was interviewed in Jun 2024. There was 1 interview round.
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I applied via Naukri.com and was interviewed in Jan 2022. There were 4 interview rounds.
I applied via Approached by Company and was interviewed in Feb 2024. There were 2 interview rounds.
I applied via Job Portal and was interviewed in Dec 2024. There was 1 interview round.
I was interviewed in Jul 2024.
Okay asked questions about ML , python , NLP , LLM
I applied via Naukri.com and was interviewed in Jul 2024. There was 1 interview round.
Developed a sentiment analysis model using NLP to analyze customer reviews for a product.
Collected and preprocessed text data from various sources
Performed tokenization, stopword removal, and lemmatization
Built a machine learning model using techniques like TF-IDF and LSTM
Evaluated the model's performance using metrics like accuracy and F1 score
Deployed the model for real-time sentiment analysis of new reviews
Cosine similarity is a measure of similarity between two non-zero vectors in an inner product space.
It measures the cosine of the angle between the two vectors.
Values range from -1 (completely opposite) to 1 (exactly the same).
Used in recommendation systems, text mining, and clustering algorithms.
Iterator is an object that allows iteration over a collection, while iterable is an object that can be iterated over.
Iterator is an object with a next() method that returns the next item in the collection.
Iterable is an object that has an __iter__() method which returns an iterator.
Example: List is iterable, while iter(list) returns an iterator.
Python function to calculate cosine similarity between two vectors.
Define a function that takes two vectors as input.
Calculate the dot product of the two vectors.
Calculate the magnitude of each vector and multiply them.
Divide the dot product by the product of magnitudes to get cosine similarity.
F1 score is a metric used to evaluate the performance of a classification model by considering both precision and recall.
F1 score is the harmonic mean of precision and recall, calculated as 2 * (precision * recall) / (precision + recall).
It is a better metric than accuracy when dealing with imbalanced datasets.
A high F1 score indicates a model with both high precision and high recall.
F1 score ranges from 0 to 1, where
I applied via Company Website and was interviewed in Apr 2024. There was 1 interview round.
I applied via Referral and was interviewed before Apr 2023. There was 1 interview round.
I applied via Approached by Company and was interviewed in Jul 2024. There were 2 interview rounds.
Developed a recommendation system for an e-commerce platform using collaborative filtering
Used collaborative filtering to analyze user behavior and recommend products
Implemented matrix factorization techniques to improve recommendation accuracy
Evaluated model performance using metrics like RMSE and precision-recall curves
I am currently working on developing machine learning models using Python, TensorFlow, and scikit-learn.
Python programming language
TensorFlow framework
scikit-learn library
I would approach a machine learning problem by first understanding the problem, collecting and preprocessing data, selecting a suitable algorithm, training the model, evaluating its performance, and fine-tuning it.
Understand the problem statement and define the objectives clearly.
Collect and preprocess the data to make it suitable for training.
Select a suitable machine learning algorithm based on the problem type (clas...
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Senior QA Engineer
3
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| ₹0 L/yr - ₹0 L/yr |
McKinsey & Company
Deloitte
Accenture
KPMG India