Machine learning for Agriculture and Natural Sciences

Explore the fundamentals of supervised learning algorithms, especially for agricultural and natural science datasets. Implement and interpret Machine Learning models, starting with statistical models. Learn how to choose and divide your datasets (testing and training). Select appropriate variables.

Then brush up on your Python and move on to Deep Learning. Learn about the XOR problem, back-propagation, and regularization. Learn what convolution is, and use it to classify images. Learn about U-Net and ResNet, and their applications in machine vision.

For syllabus and more information, see the course website.