Ernst & Young Machine Learning Engineer Interview Questions and Answers for Experienced

My projects I have worked on.

Every details about my projects machine learning algorithms I have worked on detailed discussion about the algorithms.

How to handle missing values?

How to handle outliers?

Best technique handle missing values?

Neural networks, architectures, Why and Which Neural Network you have used. Why not the other one.

Q1. What are outliers and how to handle this?
Ans. 
Outliers are data points that deviate significantly from the rest of the data. They can be handled by removing, transforming or imputing them.
• Outliers can be detected using statistical methods like Z-score, IQR, or visual methods like box plots.

• Removing outliers can lead to loss of information, so transforming or imputing them is preferred.

• Transforming outliers can be done by applying mathematical functions like log, s...read more
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Q2. What is CNN? How to use it?? No of layers you have used in your case? Ensemble techniques
Ans. 
CNN stands for Convolutional Neural Network, used for image classification and object recognition.
• CNN is a type of neural network that uses convolutional layers to extract features from images.

• It is commonly used for image classification and object recognition tasks.

• CNNs can have multiple layers, including convolutional, pooling, and fully connected layers.

• The number of layers used depends on the complexity of the task...read more
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