Module 3

Machine learning models that are trained off of images usually break the image down into smaller sections which it can then assign a value to each one individually and make a prediction for what the image is. In this activity you will become part of this process by being given a section of image to then determine what the image is and assign values that are used in machine learning such as weights.

There are a few fundamental building blocks that can apply to almost every type of machine learning model, these include algorithms, batches, epochs, activation functions and other key concepts used in all fields of machine learning. In this activity you will be learning about these fundamental concepts and how they apply to machine learning.

When a machine learning model uses images, it must process them by applying different kernels and filters to the image to make it easier to extract information from it. In this activity you will learn how machine learning models use kernel algorithms and filters to extract information from the images, how to create a kernel filter, and the different libraries and types of kernels you could use.

Machine learning models can accomplish many different tasks and are specially created to accomplish tasks, so not every model looks or works the exact same way. This activity will walk you through two different types of machine learning model that are quite common in image processing, one is a transfer learning model while the other is a logistic regression model.