Computer vision and machine learning project to classify body movement.Developed a human action recognition model using TensorFlow framework to classify 15 distinct activities with an accuracy of 82%Fine-tuned hyperparameters and leveraged transfer learning from pretrained models to optimize model generalization and accuracy.Implemented data preprocessing techniques such as resizing, normalization, and augmentation to enhance model performance.

The hydraulic arm model demonstrated the principles of fluid power in mechanical systems.Engineers used the hydraulic arm model to simulate real-world applications in industries like manufacturing and construction.Through hands-on experimentation with the hydraulic arm model, students gained a deeper understanding of hydraulic systems' functionality.