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Course Outline
Introduction
Overview of AutoML Features and Architecture
- Google’s ML ecosystem
- AutoML line of products
Working With Google’s Machine Learning Ecosystem
- Applications for AutoML products
- Challenges and limitations
Evaluating Content Using AutoML Natural Language
- Preparing datasets
- Creating and deploying models
- Text and document training (classification, extraction, analysis)
Classifying Images Using AutoML Vision
- Labeling images
- Training and evaluating models
- AutoML Vision Edge
Creating Translation Models Using AutoML Translation
- Preparing datasets (source and target language)
- Creating and managing models
- Testing models
Making Predictions from Trained Models
- Analyzing documents
- Image prediction
- Translating content
Exploring Other AutoML Products
- AutoML Tables for structured data
- AutoML Video Intelligence for videos
Troubleshooting
Summary and Conclusion
Requirements
- Basic knowledge of data analytics
- Familiarity with machine learning
Audience
- Data scientists
- Data analysts
- Developers
7 Hours
Testimonials (1)
Responses with solutions and practical use.
