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posted on 17 Jul 2022
I applied via Approached by Company and was interviewed before Jul 2021. There were 2 interview rounds.
Bagging and Boosting are ensemble learning techniques used to improve model performance.
Bagging involves training multiple models on different subsets of the data and averaging their predictions.
Boosting involves training models sequentially, with each model focusing on the errors of the previous model.
Bagging reduces variance and overfitting, while boosting reduces bias and underfitting.
Examples of bagging algorithms ...
Logistic regression is used when the dependent variable is categorical, while linear regression is used for continuous variables.
Logistic regression predicts the probability of an event occurring, while linear regression predicts the value of a dependent variable.
Logistic regression uses a sigmoid function to map the output to a probability value between 0 and 1.
Linear regression assumes a linear relationship between t...
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posted on 11 Dec 2024
I applied via LinkedIn and was interviewed in Nov 2024. There were 2 interview rounds.
There are 10 multiple-choice questions (MCQs) on Python, 20 MCQs on machine learning (ML), and 10 questions on deep learning (DL).
Overfitting in decision trees occurs when the model learns noise in the training data rather than the underlying pattern.
Overfitting happens when the decision tree is too complex and captures noise in the training data.
It leads to poor generalization on unseen data, as the model is too specific to the training set.
To prevent overfitting, techniques like pruning, setting a minimum number of samples per leaf, or using en
Bagging is a machine learning ensemble technique where multiple models are trained on different subsets of the training data and their predictions are combined.
Bagging stands for Bootstrap Aggregating.
It helps reduce overfitting by combining the predictions of multiple models.
Random Forest is a popular algorithm that uses bagging by training multiple decision trees on random subsets of the data.
A neuron is a basic unit of a neural network that receives input, processes it, and produces an output.
Neurons are inspired by biological neurons in the human brain.
They receive input signals, apply weights to them, sum them up, and pass the result through an activation function.
Neurons are organized in layers in a neural network, with each layer performing specific tasks.
In deep learning, multiple layers of neurons ar...
I applied via Hirect and was interviewed in May 2022. There were 5 interview rounds.
Basic python sql questions, mcq based and coding questions.
I applied via Referral and was interviewed in Dec 2024. There were 2 interview rounds.
15 MCQ, 2 coding round
I applied via Referral and was interviewed before Oct 2023. There was 1 interview round.
Python sql basic questions
I applied via Approached by Company
Transformers are a type of neural network architecture that utilizes self-attention mechanisms to process sequential data.
Transformers use self-attention mechanisms to weigh the importance of different input elements, allowing for parallel processing of sequences.
Unlike RNNs and LSTMs, Transformers do not rely on sequential processing, making them more efficient for long-range dependencies.
Transformers have been shown ...
Different types of Attention include self-attention, global attention, and local attention.
Self-attention focuses on relationships within the input sequence itself.
Global attention considers the entire input sequence when making predictions.
Local attention only attends to a subset of the input sequence at a time.
Examples include Transformer's self-attention mechanism, Bahdanau attention, and Luong attention.
GPT is a generative model while BERT is a transformer model for natural language processing.
GPT is a generative model that predicts the next word in a sentence based on previous words.
BERT is a transformer model that considers the context of a word by looking at the entire sentence.
GPT is unidirectional, while BERT is bidirectional.
GPT is better for text generation tasks, while BERT is better for understanding the cont
Data scientists analyze data to gain insights, machine learning (ML) involves algorithms that improve automatically through experience, and artificial intelligence (AI) refers to machines mimicking human cognitive functions.
Data scientists analyze large amounts of data to uncover patterns and insights.
Machine learning involves developing algorithms that improve automatically through experience.
Artificial intelligence r...
I applied via Company Website and was interviewed in Dec 2024. There were 2 interview rounds.
Python coding and ML resume based questions
I applied via Naukri.com and was interviewed in Jun 2024. There were 4 interview rounds.
First round is coding round where two use cases are there. Need to solve them
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