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Accuracy in machine learning measures how often the model makes correct predictions.
Accuracy is the ratio of correctly predicted instances to the total instances in the dataset.
It is a common evaluation metric for classification models.
Accuracy can be calculated using the formula: (TP + TN) / (TP + TN + FP + FN), where TP = True Positives, TN = True Negatives, FP = False Positives, FN = False Negatives.
For example, if ...
Recall is a metric in machine learning that measures the ability of a model to find all relevant cases within a dataset.
Recall is calculated as the ratio of true positive cases to the sum of true positive and false negative cases.
It is also known as sensitivity or true positive rate.
A high recall value indicates that the model is good at identifying all relevant cases, even if it means more false positives.
For example,...
I appeared for an interview in Oct 2024.
We were asked to created an application on local system using yolo for object detection and use container as well.
Decorators in Python are functions that modify the behavior of other functions.
Decorators are defined using the @decorator_name syntax before a function definition.
They can be used to add functionality to existing functions without modifying their code.
Common use cases include logging, timing, and access control.
Example: @staticmethod decorator in Python makes a method static.
I applied via LinkedIn and was interviewed in Mar 2023. There were 2 interview rounds.
I appeared for an interview before Apr 2023.
Assignment given to you over mail.
Python basic coding test
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I applied via Recruitment Consulltant and was interviewed in Jan 2022. There were 2 interview rounds.
I applied via Referral and was interviewed in Sep 2024. There was 1 interview round.
I have worked on projects related to image recognition, natural language processing, and predictive analytics using machine learning.
Developed a deep learning model for image recognition using convolutional neural networks
Implemented a sentiment analysis system using natural language processing techniques
Built a predictive analytics model for customer churn prediction in a telecom company
I have worked on projects involving natural language processing, computer vision, and predictive modeling.
Developed a sentiment analysis model using NLP techniques
Implemented a facial recognition system using computer vision algorithms
Built a predictive model for customer churn prediction
I applied via Company Website and was interviewed in Nov 2024. There were 2 interview rounds.
Logical, Verbal, reasoning 90 mins
Use a for loop to iterate through a range and print even numbers by checking divisibility by 2.
Use a for loop with a range, e.g., for i in range(10):
Check if a number is even using the modulus operator: if i % 2 == 0:
Print the even number inside the if statement.
Example: for i in range(10): if i % 2 == 0: print(i) will print 0, 2, 4, 6, 8.
based on 4 interview experiences
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Software Development Engineer
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Machine Learning Engineer
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Machine Learning Intern
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Softwaretest Engineer
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Full Stack Software Developer
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