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Course Outline
Introduction
- Comparing ML Kit, TensorFlow, and other machine learning services.
- Overview of ML Kit features and components.
Getting Started
- Setting up the ML Kit SDK.
- Exploring APIs and sample applications.
Implementing ML Kit Vision APIs
- Automating data entry through Text Recognition.
- Detecting faces for selfies and portraits (Face Detection).
- Interpreting body positions (Pose Detection).
- Applying background effects (Selfie Segmentation).
- Integrating Barcode Scanning.
- Identifying objects, locations, species, etc. (Image Labeling).
- Locating prominent objects in an image (Object Detection and Tracking).
- Recognizing handwritten text (Digital Ink Recognition).
Working with Natural Language APIs
- Identifying languages.
- Translating texts.
- Generating smart replies.
- Utilizing entity extraction.
Optimizing Existing Apps with ML Kit
- Using custom models with ML Kit.
- Migrating from Firebase to the new ML Kit SDK.
- Migrating from Mobile Vision to ML Kit SDK.
- Reducing app size for deployment.
- Refactoring apps to utilize dynamic feature modules.
Troubleshooting Tips
Summary and Next Steps
Requirements
- A foundational understanding of machine learning concepts.
- Prior experience in mobile development.
Target Audience
- Software Engineers.
- Mobile Application Developers.
14 Hours
