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Course Outline
Introduction
- Comparing ML Kit with TensorFlow and other machine learning services
- Overview of ML Kit features and core components
Getting Started
- Configuring 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 via Face Detection
- Analyzing body positions with Pose Detection
- Creating background effects using Selfie Segmentation
- Integrating Barcode Scanning capabilities
- Identifying objects, locations, species, etc., through Image Labeling
- Locating prominent objects in images via Object Detection and Tracking
- Recognizing handwritten text through Digital Ink Recognition
Working with Natural Language APIs
- Identifying languages
- Translating texts
- Generating smart replies
- Utilizing entity extraction
Optimizing Existing Apps with ML Kit
- Utilizing custom models with ML Kit
- Migrating from Firebase to the new ML Kit SDK
- Migrating from Mobile Vision to the ML Kit SDK
- Reducing application size for deployment
- Refactoring apps to incorporate dynamic feature modules
Troubleshooting Tips
Summary and Next Steps
Requirements
- A foundational understanding of machine learning concepts
- Prior experience in mobile application development
Target Audience
- Software Engineers
- Mobile Application Developers
14 Hours
