Executive Development Programme in Image Classification
-- ViewingNowThe Executive Development Programme in Image Classification is a certificate course that holds immense importance and industry demand in today's data-driven world. This program equips learners with essential skills to analyze and classify images, a critical aspect of various industries such as healthcare, security, and autonomous vehicles.
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⢠Image Classification Fundamentals: Understanding the basics of image classification, including differentiating between image classification and image recognition, and the importance of image classification in various industries.
⢠Convolutional Neural Networks (CNNs): Learning about the architecture of CNNs, including convolutional layers, pooling layers, and fully connected layers, and their role in image classification.
⢠Training and Validation Techniques: Exploring best practices in training and validating image classification models, including data preprocessing, regularization techniques, and hyperparameter tuning.
⢠Transfer Learning and Deep Learning: Understanding how to leverage pre-trained models and transfer learning to improve image classification accuracy, as well as the principles of deep learning and its application in image classification.
⢠Object Detection and Localization: Learning about object detection and localization techniques, including techniques for detecting multiple objects in a single image, and their application in image classification.
⢠Evaluation Metrics: Understanding the different evaluation metrics used to assess the performance of image classification models, including accuracy, precision, recall, and F1 score.
⢠Real-World Applications: Exploring real-world applications of image classification, including facial recognition, medical imaging, and autonomous vehicles, and the challenges and opportunities presented by these applications.
⢠Ethics and Bias in Image Classification: Examining the ethical considerations and potential biases in image classification, including issues related to data privacy, fairness, and accountability.
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