Filter interviews by
I applied via Company Website and was interviewed before Apr 2023. There was 1 interview round.
Neural networks are a type of machine learning algorithm inspired by the human brain, consisting of interconnected nodes that process information.
Neural networks consist of layers of interconnected nodes called neurons.
Each neuron receives input, processes it using an activation function, and passes the output to the next layer.
Neural networks learn by adjusting the weights of connections between neurons during trainin...
Boosting algorithms are ensemble learning techniques that combine multiple weak learners to create a strong learner.
Boosting algorithms train models sequentially, with each model correcting errors made by the previous one.
Examples of boosting algorithms include AdaBoost, Gradient Boosting, and XGBoost.
AdaBoost adjusts the weights of incorrectly classified instances, Gradient Boosting fits new models to the residuals of...
I applied via Company Website and was interviewed in Oct 2023. There were 3 interview rounds.
I applied via LinkedIn and was interviewed in Apr 2024. There was 1 interview round.
It was 45 min interview based on mathematical, probablity, statistical, machine leaning based questions
I applied via Campus Placement and was interviewed in Oct 2022. There were 4 interview rounds.
It has SAS python SQL aptitude logic
Was given a case study on how should fedex should grow inorganic or organic
Cross validation is a technique used to evaluate the performance of a machine learning model by testing it on multiple subsets of the data.
It involves dividing the data into multiple subsets or folds.
The model is trained on a subset and tested on the remaining subset.
This process is repeated for all subsets and the results are averaged to get a final performance metric.
It helps to prevent overfitting and provides a mor...
A confusion matrix is a table used to evaluate the performance of a classification model.
It shows the number of true positives, true negatives, false positives, and false negatives.
It helps in calculating various evaluation metrics like accuracy, precision, recall, and F1 score.
It is useful in comparing the performance of different models.
Example: A confusion matrix for a binary classification problem can be represente...
I applied via campus placement at Gokhale Institute of Politics and Economics, Pune and was interviewed before May 2023. There were 3 interview rounds.
It was on python ,SQL ,logical reasoning and quant
Case study was given on a business scenario
Cross validation is a technique to evaluate the performance of a model by splitting the data into multiple subsets.
Cross validation helps in assessing how well a model generalizes to new data.
It involves splitting the data into training and testing sets multiple times to get a more reliable estimate of model performance.
Common types of cross validation include k-fold cross validation and leave-one-out cross validation.
Confusion Matrix is a table used to describe the performance of a classification model.
Confusion Matrix is a 2x2 table with Actual and Predicted values
It consists of True Positive, True Negative, False Positive, and False Negative
Example: TP=100, TN=50, FP=10, FN=5
Accuracy = (TP + TN) / (TP + TN + FP + FN)
Precision = TP / (TP + FP)
Recall = TP / (TP + FN)
Coordinator
351
salaries
| ₹4 L/yr - ₹7.5 L/yr |
Senior Administrative Assistant
148
salaries
| ₹3.5 L/yr - ₹6.2 L/yr |
Administration Assistant
105
salaries
| ₹2.8 L/yr - ₹5.2 L/yr |
Team Lead
103
salaries
| ₹5.5 L/yr - ₹10.4 L/yr |
Finance and Administration Assistant
91
salaries
| ₹3 L/yr - ₹5.2 L/yr |
FedEx Express
DHL Express
Blue Dart Express
GATI-KWE