Infocusp Machine Learning Engineer Interview Questions and Answers

Q1. Why did you choose Yolo over two-stage detectors?
Ans. 
Chose Yolo for its real-time processing speed and simplicity compared to two-stage detectors.
• Yolo is faster than two-stage detectors as it processes the image in a single pass

• Yolo is simpler to implement and train compared to two-stage detectors like Faster R-CNN

• Yolo is more suitable for real-time applications where speed is crucial, such as autonomous driving or video surveillance
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Q2. What was your recent project?
Ans. 
Developed a machine learning model to predict customer churn for a telecom company.
• Used Python and scikit-learn for data preprocessing and model building

• Performed feature engineering to extract relevant information from customer data

• Evaluated model performance using metrics like accuracy, precision, and recall
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Q3. Implement IOU function
Ans. 
Implement IOU function for evaluating object detection models.
• Calculate the area of overlap between two bounding boxes.

• Calculate the area of union between two bounding boxes.

• Divide the area of overlap by the area of union to get IOU.

• IOU = Area of Overlap / Area of Union

• Example: Bounding Box 1 (x1, y1, x2, y2) = (0, 0, 4, 4), Bounding Box 2 (x1, y1, x2, y2) = (2, 2, 6, 6)
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Q4. Implement Dataset for object detection
Ans. 
Implementing a dataset for object detection involves collecting and labeling images with bounding boxes around objects of interest.
• Collect a diverse set of images containing the objects you want to detect

• Label the objects in the images with bounding boxes to indicate their location

• Split the dataset into training, validation, and test sets for model evaluation

• Augment the dataset by applying transformations like rotation...read more
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Q1. Why do you want to join infocusp?
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There were two Python questions, difficulty level was average.