What sounded simple in theory is also simple in practice. Add a machine learning model to your project with a simple drag and drop. Learn about the information the model provided for you once added to your Xcode project, and how automated class generation works. Also prepare the user input for being processed by Core ML and your machine learning model.
- [Instructor] You already know…that when it comes to dealing with Core ML,…then we also need to work with ML model files…which hold the machine-learning models that we want to use.…So for our purpose to identify the gender of a given name,…we have a task-specific machine learning model…that you will find in your exercise files,…and it's called GenderByName.…And all we really need to do with this model…to work with it in Xcode,…is drag it from our folder into our Xcode project,…make sure that we select Destination: Copy Items, if needed,…and also that we add this to our target,…which is our next NamesML project.…
If I hit Finish now, it is added to my Project Navigator,…and if I now open up my GenderByName model,…then we get a lot of information already.…So we get its name, we get its type,…which is Pipeline Classifier,…we also get the author, a description and a license.…And we also get is a lot of information…about the parameters that we have to use here.…So these parameters are divided into two sections,…the input section and the output section.…
- What are machine learning, Core ML, Vision, and NLP?
- Adding a machine learning model to a project
- Getting predictions from machine learning models
- Converting existing machine learning models for Core ML
- Classifying images and detecting objects with Vision and Core ML
- Analyzing natural language text with NSLinguisticTagger