Course: Machine Learning The dataset is Sign Language MNIST (https://www.kaggle.com/datasets/datamunge/sign-language-mnist). The original MNIST image dataset of handwritten digits is a popular benchmark for image-based machine learning methods but researchers have renewed efforts to update it and develop drop-in replacements that are more challenging for computer vision and original for real-world applications. The dataset format is patterned to match closely with the classic MNIST. Each training and test case represents a label (0-25) as a one-to-one map for each alphabetic letter A-Z (and no cases for 9=J or 25=Z because of gesture motions). The training data (27,455 cases) and test data (7172 cases) are approximately half the size of the standard MNIST but otherwise similar with a header row of label, pixel1,pixel2….pixel784 which represent a single 28x28 pixel image with grayscale values between 0-255.
The first task is to train two models to recognize sign language letters and compare their performances.
- Logistic regression and CNN were trained.
use the classifiers trained in the first task to identify a series of
Check the results in the report.