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3 Leet code mediums in 30 mins.
LC mediums refer to LeetCode mediums, which are medium difficulty coding problems on the LeetCode platform.
LC mediums are coding problems with medium difficulty level on LeetCode platform.
Solving 3 LC mediums in 30 minutes requires good problem-solving skills and efficient coding techniques.
Examples of LC mediums include 'Longest Substring Without Repeating Characters' and 'Container With Most Water'.
I applied via Referral and was interviewed before Jun 2023. There were 3 interview rounds.
Python for Data Science basics
Optimization case study
I applied via Referral and was interviewed before Sep 2022. There were 6 interview rounds.
Had to share my screen and they gave live problems to test my knowledge in python
What people are saying about Walmart
I was interviewed in Apr 2021.
Hyperparameters of XGBoost, Random Forest, and SVM can be tuned using techniques like grid search, random search, and Bayesian optimization.
For XGBoost, important hyperparameters to tune include learning rate, maximum depth, and number of estimators.
For Random Forest, important hyperparameters to tune include number of trees, maximum depth, and minimum samples split.
For SVM, important hyperparameters to tune include ke...
Hyperparameters are settings that control the behavior of machine learning algorithms.
Hyperparameters are set before training the model.
They control the learning process and affect the model's performance.
Examples include learning rate, regularization strength, and number of hidden layers.
Optimizing hyperparameters is important for achieving better model accuracy.
Ridge and LASSO are regularization techniques used in linear regression to prevent overfitting.
Ridge adds a penalty term to the sum of squared errors, which shrinks the coefficients towards zero but doesn't set them exactly to zero.
LASSO adds a penalty term to the absolute value of the coefficients, which can set some of them exactly to zero.
The geometric interpretation of Ridge is that it adds a constraint to the size...
Steps to fit a time series model
Identify the time series pattern
Choose a suitable model
Split data into training and testing sets
Fit the model to the training data
Evaluate model performance on testing data
Refine the model if necessary
Forecast future values using the model
RNN and CNN are neural network architectures used for different types of data.
RNN is used for sequential data like time series, text, speech, etc.
CNN is used for grid-like data like images, videos, etc.
RNN has feedback connections while CNN has convolutional layers.
RNN can handle variable length input while CNN requires fixed size input.
Both can be used for classification, regression, and generation tasks.
Answering a question on data and objective function for cost and revenue optimization case studies.
For cost optimization, look at data related to expenses, production costs, and resource allocation.
For revenue optimization, look at data related to sales, customer behavior, and market trends.
Objective function for cost optimization could be minimizing expenses while maintaining quality.
Objective function for revenue opt...
Walmart interview questions for designations
PCA is a dimensionality reduction technique, decision tree is a classification algorithm, and computer vision is a field of study focused on enabling computers to interpret and understand visual information.
PCA is used to reduce the number of variables in a dataset while retaining the most important information.
Decision trees are used to classify data based on a set of rules and conditions.
Computer vision involves usin...
I applied via Approached by Company and was interviewed before Sep 2021. There were 3 interview rounds.
Explain dynamic programming with memoization
I applied via Campus Placement and was interviewed before Sep 2020. There were 3 interview rounds.
I applied via Approached by Company and was interviewed in Aug 2023. There was 1 interview round.
Logistic regression can be applied for multiclasstext classification by using one-vs-rest or softmax approach.
One-vs-rest approach: Train a binary logistic regression model for each class, treating it as the positive class and the rest as the negative class.
Softmax approach: Use the softmax function to transform the output of the logistic regression into probabilities for each class.
Evaluate the model using appropriate...
I applied via LinkedIn and was interviewed before Apr 2023. There was 1 interview round.
fbprophet is a forecasting model developed by Facebook that uses time series data to make predictions.
fbprophet is an open-source forecasting tool developed by Facebook's Core Data Science team.
It is based on an additive model where non-linear trends are fit with yearly, weekly, and daily seasonality, plus holiday effects.
fbprophet can be used to forecast traffic by providing historical data on traffic patterns and usi...
I applied via Company Website and was interviewed before Jul 2023. There were 3 interview rounds.
Binary tree question was asked
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