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posted on 14 Jul 2024
Logistic regression is used for binary classification while linear regression is used for regression tasks.
Logistic regression predicts the probability of a binary outcome (0 or 1) based on input features.
Linear regression predicts a continuous value based on input features.
Logistic regression uses a sigmoid function to map predicted values between 0 and 1.
Linear regression uses a linear equation to model the relations...
Loss functions are used to measure the difference between predicted values and actual values in machine learning models.
Loss functions quantify how well a model is performing by comparing predicted values to actual values
Common loss functions include Mean Squared Error (MSE), Cross Entropy Loss, and Hinge Loss
Different loss functions are used for different types of machine learning tasks, such as regression or classifi
Write the code for logistic Regression
I applied via Internshala and was interviewed before Aug 2022. There were 2 interview rounds.
Bagging and boosting are ensemble learning techniques. Tree based models are decision trees used for classification and regression.
Bagging (Bootstrap Aggregating) involves training multiple models on different subsets of the training data and combining their predictions.
Boosting involves training multiple models sequentially, with each model correcting the errors of its predecessor.
Different types of learning models in...
KNN is a supervised machine learning algorithm used for classification and regression. K-means is an unsupervised clustering algorithm.
KNN stands for K-Nearest Neighbors and works by finding the K closest data points to a given data point to make predictions.
K-means is a clustering algorithm that partitions data into K clusters based on similarity.
KNN is used for classification tasks, while K-means is used for clusteri...
Supervised learning is a type of machine learning where the model is trained on labeled data.
In supervised learning, the algorithm learns from labeled training data to make predictions or decisions.
It involves mapping input data to the correct output label based on the input-output pairs provided during training.
Common examples include classification and regression tasks, such as predicting whether an email is spam or ...
Unsupervised learning is a type of machine learning where the model learns patterns from unlabeled data.
No explicit labels are provided in unsupervised learning
The model must find patterns and relationships in the data on its own
Clustering and dimensionality reduction are common techniques in unsupervised learning
Random forest is an ensemble learning method that builds multiple decision trees and merges them to improve accuracy and prevent overfitting.
Random forest is a collection of decision trees that are trained on random subsets of the data.
Each tree in the random forest independently predicts the target variable, and the final prediction is made by averaging the predictions of all trees.
Random forest is effective in handli...
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I applied via Approached by Company and was interviewed in Dec 2023. There was 1 interview round.
I applied via Campus Placement and was interviewed in Jun 2024. There were 2 interview rounds.
Some basic aptitude questions were asked , but had to be solved in 20 minutes
Medium level 2 leet code questions were asked and i cleared both
I applied via Campus Placement and was interviewed in Dec 2023. There was 1 interview round.
Large Language Models are advanced AI models that can generate human-like text based on input data.
Large Language Models use deep learning techniques to understand and generate text.
Examples include GPT-3 (Generative Pre-trained Transformer 3) and BERT (Bidirectional Encoder Representations from Transformers).
They are trained on vast amounts of text data to improve their language generation capabilities.
RAGs stands for Red, Amber, Green. It is a project management tool used to visually indicate the status of tasks or projects.
RAGs is commonly used in project management to quickly communicate the status of tasks or projects.
Red typically indicates tasks or projects that are behind schedule or at risk.
Amber signifies tasks or projects that are on track but may require attention.
Green represents tasks or projects that ar...
There is no one-size-fits-all answer as the best clustering algorithm depends on the specific dataset and goals.
The best clustering algorithm depends on the dataset characteristics such as size, dimensionality, and noise level.
K-means is popular for its simplicity and efficiency, but may not perform well on non-linear data.
DBSCAN is good for clusters of varying shapes and sizes, but may struggle with high-dimensional d...
posted on 13 Mar 2024
I was interviewed before Mar 2023.
I applied via Campus Placement and was interviewed before Feb 2020. There were 4 interview rounds.
I applied via Campus Placement and was interviewed in Dec 2020. There was 1 interview round.
I applied via Campus Placement and was interviewed before Jun 2020. There were 3 interview rounds.
I was interviewed before Jun 2021.
Round duration - 180 minutes
Round difficulty - Easy
It was an mcq + coding round. There were aptitude and ouput based question in mcq. And coding questions were easy
Given an integer array 'ARR' of size 'N' containing numbers from 0 to (N - 2). Each number appears at least once, and there is one number that appears twice. Yo...
Find the duplicate number in an array of integers from 0 to (N-2).
Iterate through the array and keep track of the frequency of each number using a hashmap.
Return the number with a frequency greater than 1 as the duplicate number.
Time complexity can be optimized to O(N) using Floyd's Tortoise and Hare algorithm.
You are provided with a string S
and an array of integers A
of size M
. Your task is to perform M
operations on the string as specified by the indices in array A
...
Given a string and an array of indices, reverse substrings based on the indices to obtain the final string.
Iterate through the array of indices and reverse the substrings accordingly
Ensure the range specified by each index is non-empty
Return the final string after all operations are completed
Round duration - 60 Minutes
Round difficulty - Easy
It was technical + hr round. there were 2 people as interviewer. They stated from intro and asked some basic puzzles and hr questions. After that they asked about my projects, technologies and some ds algo and dbms questions.
Tip 1 : Practice aptitude
Tip 2 : Focus on practicing coding
Tip 3 : Learn from mistakes
Tip 1 : Mention some projects that you have done
Tip 2 : Try to have skills that are required for the role
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