Course Outline
Introduction
- ML Kit vs TensorFlow vs other machine learning services
- Overview of ML Kit features and components
Getting Started
- Setting up the ML Kit SDK
- Exploring APIs and sample apps
Implementing ML Kit Vision APIs
- Automating data entry (Text Recognition)
- Detecting faces for selfies and portraits (Face Detection)
- Interpreting body positions (Pose Detection)
- Adding background effects (Selfie Segmentation)
- Integrating Barcode Scanning
- Identifying objects, places, species, etc. (Image Labeling)
- Locating prominent objects in an image (Object Detection and Tracking)
- Recognizing handwritten texts (Digital Ink Recognition)
Working with Natural Language APIs
- Identifying languages
- Translating texts
- Generating smart replies
- Using 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 use dynamic feature modules
Troubleshooting Tips
Summary and Next Steps
Requirements
- An understanding of machine learning
- Experience with mobile development
Audience
- Software Engineers
- Mobile App Developers
Testimonials (4)
The details and the presentation style.
Cristian Mititean - Accenture Industrial SS
Course - Azure Machine Learning (AML)
Working from first principles in a focused way, and moving to applying case studies within the same day
Maggie Webb - Department of Jobs, Regions, and Precincts
Course - Artificial Neural Networks, Machine Learning, Deep Thinking
That it was applying real company data. Trainer had a very good approach by making trainees participate and compete
Jimena Esquivel - Zakład Usługowy Hakoman Andrzej Cybulski
Course - Applied AI from Scratch in Python
Keeping it short and simple. Creating intuition and visual models around the concepts (decision tree graph, linear equations, calculating y_pred manually to prove how the model works).