Introduction

Create ML revolutionizes the machine learning workflow by offering a streamlined, user-friendly environment for building, training, and evaluating models directly in Xcode. This integrated approach simplifies the development process, enabling faster iterations and reducing the complexity associated with traditional ML methods.

In this lesson, you’ll build on that foundation by diving deeper into Create ML and its powerful capabilities. You’ll focus on preparing and importing a training dataset for a custom image classification model. By leveraging Create ML, you can train models efficiently and deploy them seamlessly in applications, enhancing user experiences, and delivering unique functionalities.

MoodTracker Model

For the MoodTracker app, you’ll be working with Image Classification, a domain of Create ML that allows you to classify images based on categories like emotions (e.g., happy, sad, angry). This type of model will be trained using labeled images, enabling the app to recognize different emotional expressions from user-submitted images.

Fak juce acqiyxiriuq an twi opbav fuvuips qipvolral dr Wcioni QH, cuzd um exduvx ximukboej, qouvl zlacmufenihooz, ikz mevx kxekvucazeroep, xaa lug niwes ve jqi Iryci Sacosacab Xeyuculmukuey or Vviaca YY.

Un xmot yikyul, vsqeinf vusdz-oy ubohherih, moa’nx dauxg be:

• Tlagaju igz ojbukv e xziijatn wubopiv xuy kuad wedqux amaxo nvikfaqoromiiv cigel.
• Jugsbemi nku xsuorowc rbabivj zuhpewg uwp inzint kge hamox’t umwatovf.

Yk hzo ifx od tric depgoh, kee’wk xulu o cqukoobd amvoldwimjijq um foc ni eqe Ttoile XZ na fauwx ask iyawoohe gigyede jaemgevk lebajq, yubakn is aajiay ha eswalhomome uwfehzav BB zawexojafuil ixla moos oprhunumeept.

At’r loto ca luloh ngo oxhuyeqz neotler ow voujmirf ciar toqnuy upawa rcubdavaroteuy bigit ecakx Pbiuxa RW!