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I applied via Campus Placement and was interviewed before Sep 2020. There were 3 interview rounds.
Python code to find the root of a number
Use the math module to access the sqrt() function
Use the ** operator to raise the number to the power of 1/n
Handle negative numbers by converting them to complex numbers
General Aptitude( Probability dominats) and objective coding
Moderate levels coding questions Refer hacker rank
I applied via Campus Placement and was interviewed before May 2023. There were 3 interview rounds.
We had questions on aptitude - like upstream, downstream problems, etc.
Live coding using Python/CPP
Practice coding challenges on platforms like LeetCode or HackerRank to improve coding skills
Understand data structures and algorithms to solve coding problems efficiently
Use online IDEs like Repl.it or CodePen for live coding sessions
I applied via Campus Placement and was interviewed before Nov 2022. There were 4 interview rounds.
Probability, coding, coordinate geometry , debugging, predict the output
1 hour test 3 questions , one was on numbers I don't remember much , random people get selected in 3rd round in campus placements
I applied via Naukri.com and was interviewed before Jun 2022. There were 3 interview rounds.
Prime number in javascript
I applied via Campus Placement
Aptitude ques based on straight line, circle,pnc, probability
Coding ques level was medium to tough. Had to solve 3 ques out of 5
Rotate a 2D array by 90 degrees clockwise or counterclockwise.
Transpose the matrix by swapping elements across the diagonal
Reverse each row or column depending on clockwise or counterclockwise rotation
Example: [[1,2],[3,4]] rotated clockwise becomes [[3,1],[4,2]]
I applied via Naukri.com and was interviewed before Oct 2022. There were 3 interview rounds.
One simple coding test and general aptitute
I applied via Referral and was interviewed in Jul 2021. There were 4 interview rounds.
C is the regularization parameter while gamma controls the shape of the decision boundary in SVM.
C controls the trade-off between achieving a low training error and a low testing error.
A smaller C value creates a wider margin and allows more misclassifications.
Gamma controls the shape of the decision boundary and the influence of each training example.
A smaller gamma value creates a smoother decision boundary while a l...
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