Main Purpose
The main purpose of Teachable Machine is to provide a web tool that allows users to create machine learning models without any coding knowledge. It aims to make machine learning accessible and easy for everyone by enabling users to train their own models to recognize images, sounds, and poses.
Key Features
- No Coding Required: Teachable Machine eliminates the need for coding, making it accessible to users without programming experience.
- Image Recognition: Users can train their machine learning models to recognize and classify images based on specific categories.
- Sound Recognition: Teachable Machine also supports training models to recognize and classify sounds, expanding its capabilities beyond visual data.
- Pose Recognition: Users can train their models to recognize and classify different poses, such as standing or sitting down.
- Export Models: Once the training is complete, users can export their machine learning models for use in websites, apps, or physical machines.
Use Case
- Image Classification: Teachable Machine can be used by individuals or businesses that need to classify and categorize images automatically, such as organizing photo collections or analyzing visual data.
- Sound Classification: It is useful for applications that require sound recognition, such as identifying specific sounds in audio recordings or creating interactive audio experiences.
- Pose Detection: Teachable Machine can be utilized in applications that require pose detection, such as fitness tracking or gesture-based control systems.
