📚 Syllabus & Course Curriculum
Data Science & Analytics
Module-by-module breakdown of AI for Pest and Disease Detection: Build an Image Classifier, from foundations to a certified capstone project.
Outline
Develop a comprehensive understanding of visual computing concepts and their applications in AI for pest and disease detection • Analyze the fundamentals of computer vision and machine learning to build a strong foundation for image classification • Design and implement basic image processing techniques using Python and OpenCV to enhance image quality and prepare datasets
Outline
Implement image augmentation techniques to increase dataset diversity and reduce overfitting in image classification models • Evaluate the effectiveness of various feature extraction methods, including convolutional neural networks (CNNs) and transfer learning • Configure and optimize image processing pipelines using Python and scikit-image to improve image classification accuracy
Outline
Design and implement CNN architectures using TensorFlow and Keras to classify pest and disease images • Analyze the performance of transfer learning models, including VGG16 and ResNet50, for image classification tasks • Develop and evaluate custom CNN models using Python and PyTorch to improve image classification accuracy
Outline
Implement object detection algorithms, including YOLO and SSD, to detect pests and diseases in images • Evaluate the effectiveness of image segmentation techniques, including U-Net and Mask R-CNN, for pixel-level classification • Configure and optimize object detection and segmentation pipelines using Python and OpenCV to improve detection accuracy
Outline
Develop and implement video analysis pipelines using Python and OpenCV to detect pests and diseases in real-time • Analyze the performance of temporal models, including LSTM and GRU, for video classification tasks • Configure and optimize real-time processing pipelines using Python and PyTorch to improve video analysis accuracy
Outline
Implement model optimization techniques, including pruning and knowledge distillation, to reduce model size and improve inference speed • Evaluate the effectiveness of model quantization methods, including post-training quantization and quantization-aware training • Configure and deploy optimized models on edge devices using Python and TensorFlow Lite to improve real-time processing performance
Outline
Develop and implement AI-powered solutions for pest and disease detection in various industries, including agriculture and forestry • Analyze the effectiveness of AI models in real-world applications and identify areas for improvement • Design and propose novel AI-powered solutions for emerging industry challenges and applications
Earn government-registered certification in AI for Pest and Disease Detection: Build an Image Classifier
e-Certificate and e-Marksheet issued on successful completion.
