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I applied via Approached by Company and was interviewed in Mar 2023. There were 3 interview rounds.
Develop an ML solution from the given problem statement and data.
GBM is a machine learning algorithm that builds multiple decision trees to predict outcomes and combines them to improve accuracy.
GBM stands for Gradient Boosting Machine
It builds multiple decision trees sequentially, each one correcting errors made by the previous tree
The final prediction is a combination of predictions from all the trees
GBM is an ensemble learning technique that is popular for its high accuracy and f
I applied via LinkedIn and was interviewed in Aug 2022. There were 5 interview rounds.
It was the assignment including the data science problem and SQL questions for one of the datetime table. Assignment was for 3 hours and had to complete some mandatory questions from data science assignment.
Softmax and sigmoid are both activation functions used in neural networks.
Softmax is used for multi-class classification problems, while sigmoid is used for binary classification problems.
Softmax outputs a probability distribution over the classes, while sigmoid outputs a probability for a single class.
Softmax ensures that the sum of the probabilities of all classes is 1, while sigmoid does not.
Softmax is more sensitiv...
I was asked Python, sql, coding questions
Case study on how would you identify the total number of footfall on a airport
I applied via Company Website and was interviewed before Feb 2023. There was 1 interview round.
Use string manipulation to efficiently extract numbers before the decimal point from a list of decimal numbers.
Split each decimal number by the decimal point and extract the number before it
Use regular expressions to match and extract numbers before the decimal point
Iterate through the list and extract numbers using string manipulation functions
I applied via Company Website and was interviewed before Aug 2023. There were 2 interview rounds.
Bert and transformer are models used in natural language processing for tasks like text classification and language generation.
Bert (Bidirectional Encoder Representations from Transformers) is a transformer-based model developed by Google for NLP tasks.
Transformer is a deep learning model architecture that uses self-attention mechanisms to process sequential data like text.
Both Bert and transformer have been widely use...
NLP pre processing techniques involve cleaning and preparing text data for analysis.
Tokenization: breaking text into words or sentences
Stopword removal: removing common words that do not add meaning
Lemmatization: reducing words to their base form
Normalization: converting text to lowercase
Removing special characters and punctuation
I applied via Campus Placement
Developed machine learning models to predict customer churn and optimize marketing campaigns.
Built predictive models using Python and scikit-learn
Utilized SQL to extract and manipulate data for analysis
Collaborated with cross-functional teams to implement data-driven solutions
I appeared for an interview before Apr 2023.
Python coding question and ML question
based on 2 interviews
Interview experience
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