Android 11 - Week 2 - Machine Learning
Machine learning provides your apps with the ability to progressively learn and improve from experience. This pathway introduces you to the wide variety of machine learning tools and methods Android 11 provides.
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11 Weeks of Android - Machine Learning
Watch this introductory teaser to prepare for the activities in this pathway.
#AndroidDevChallenge - Helpful innovation, powered by machine learning
The Android Developer Challenge, with a focus on “Helpful Innovation,” spotlights developers creating incredible experiences powered by on-device machine learning. Take a look at some of Google’s tools for building on-device machine learning into your apps.
CameraX + OCR
This codelab introduces you to ML Kit, a mobile SDK that brings Google’s machine learning capabilities to your Android apps. You’ll build an Android app with ML Kit using the ML Kit Text Recognition on-device API to recognize and translate text from real-time camera feed.
Create your own custom model with TensorFlow Model Maker & Android Studio
This codelab teaches you to integrate a TensorFlow Lite model that recognizes custom images from your Android app using the new ML Model Binding plugin in Android Studio.
TF Hub + ML Kit swappable model
In this video, you’ll go through the steps of searching for the model you want on tfhub.dev, downloading it, and running it with ML Kit object detection and tracking functionality for custom models.
Design for Machine Learning
Learn from the real life example of Google’s “Learn to Read” app and see how you can utilize the People + AI Guidebook to achieve an AI first design.
Android Developer Challenge winner “Stila” uses ML Kit to help monitor stress
Stila is an app that helps monitor and track your body’s stress levels so you can better understand and manage stress in your life. Take a look at how Android Developer Challenge winner Yingding Wang used ML Kit to help power this experience.
Take the Machine Learning quiz to earn a badge.
Assess your knowledge of Android 11’s machine learning tools and techniques and be recognized with the machine learning pathway badge.