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It was quite easy it's the 1:30 hr time based test with no negative marking and the total of 60 questions
I applied via campus placement at Mumbai University and was interviewed in Jan 2023. There were 2 interview rounds.
It was held at my college campus.
First round was aptitude test, can easily score
Second was Hr round (final)
Not a single question was asked on technical
Nothing much basic mathematical questions
Fascinated by the insights and patterns that can be extracted from data to drive decision-making and innovation.
Passionate about uncovering hidden trends and correlations in data
Excited about using data to solve complex problems and make informed decisions
Believe in the power of data-driven decision-making to drive business success
I applied via Instagram
Aptitude test regarding the maths and others
TuringMinds interview questions for designations
I applied via campus placement at SBCE Sree Buddha College of Engineering, Alappuzha and was interviewed in Feb 2023. There were 2 interview rounds.
Basic questions involving age, patterns, profit/loss, trains, boats/streams, positions
I applied via Company Website and was interviewed in Aug 2022. There were 3 interview rounds.
Reasoning,time and distance, apptitude,paragraphs
Top trending discussions
I was interviewed in Aug 2024.
I applied via Naukri.com and was interviewed in Mar 2024. There were 3 interview rounds.
Machine learning algorithms are tools used to analyze data, identify patterns, and make predictions without being explicitly programmed.
Machine learning algorithms can be categorized into supervised, unsupervised, and reinforcement learning.
Examples of machine learning algorithms include linear regression, decision trees, support vector machines, and neural networks.
These algorithms require training data to learn patte...
Developing a credit risk model involves several steps to assess the likelihood of a borrower defaulting on a loan.
1. Define the problem and objectives of the credit risk model.
2. Gather relevant data such as credit history, income, debt-to-income ratio, etc.
3. Preprocess the data by handling missing values, encoding categorical variables, and scaling features.
4. Select a suitable machine learning algorithm such as logi...
AIC and BIC are statistical measures used for model selection in the context of regression analysis.
AIC (Akaike Information Criterion) is used to compare the goodness of fit of different models. It penalizes the model for the number of parameters used.
BIC (Bayesian Information Criterion) is similar to AIC but penalizes more heavily for the number of parameters, making it more suitable for model selection when the focus...
XGBoost is a popular gradient boosting library while LightGBM is a faster and more memory-efficient alternative.
XGBoost is known for its accuracy and performance on structured/tabular data.
LightGBM is faster and more memory-efficient, making it suitable for large datasets.
LightGBM uses a histogram-based algorithm for splitting whereas XGBoost uses a level-wise tree growth strategy.
Count the number of duplicate words in a string.
Split the string into words using a delimiter like space or punctuation.
Create a dictionary to store the count of each word.
Iterate through the words and increment the count in the dictionary.
Count the number of words with count greater than 1 as duplicates.
Chunking in LLM refers to breaking down text into smaller chunks for better processing by the language model.
Chunking helps improve the efficiency of the language model by breaking down large text inputs into smaller segments.
It can help the model better understand the context and relationships within the text.
Chunking is commonly used in natural language processing tasks such as text summarization and sentiment analys
60 min hackerrank test,with one mysql medium difficulty question and one python medium/little hard level difficulty
based on 8 interviews
3 Interview rounds
based on 42 reviews
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