Teachable Machine - Free no-code machine learning tool for everyone

What is Teachable Machine

Have you ever wondered how machines learn to recognize your voice, identify your face, or understand your gestures? If so, you're not alone. For most people, machine learning remains an abstract concept—one that seems to require advanced mathematics, programming expertise, and expensive software. This barrier has made it incredibly difficult for educators, students, and curious minds to actually get hands-on experience with AI technology.

That's exactly where Teachable Machine comes in. Developed by Google Creative Lab, Teachable Machine is a free, web-based tool that makes creating machine learning models fast, easy, and accessible to everyone. Whether you're a teacher introducing AI concepts to a classroom, a student exploring machine learning for the first time, or a maker looking to add intelligence to your hardware projects, this tool removes the traditional barriers to entry.

The core philosophy behind Teachable Machine is remarkably simple: anyone should be able to teach a computer to recognize patterns without writing a single line of code. You don't need to install software, set up development environments, or understand complex algorithms. Everything runs directly in your web browser, making it truly accessible with just an internet connection and a modern browser like Chrome, Firefox, or Safari.

Perhaps most importantly, Teachable Machine respects your privacy. The tool offers an "entirely device-based" mode where data from your webcam and microphone never leaves your computer. This makes it particularly valuable for educational settings and privacy-conscious users who want to experiment with machine learning without concerns about data security.

Since its launch in 2017 and subsequent major update in 2022, Teachable Machine has been adopted by millions of educators, students, and creators worldwide. It's been integrated into AI education programs by MIT Media Lab and Ready AI, used by makers to create innovative hardware projects, and even employed to develop assistive technologies for people with disabilities. The tool has proven that machine learning education doesn't have to be complicated or inaccessible.

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Core Features of Teachable Machine

Let's explore the key capabilities that make Teachable Machine such a powerful learning tool. Understanding these features will help you see exactly what you can build and how the tool works under the hood.

Image Classification

One of the most popular features allows you to teach a model to recognize and classify images. Using files from your computer or live capture from your webcam, you can create classifiers that identify objects, colors, facial expressions, or any visual pattern you choose. The underlying technology uses TensorFlow.js, Google's open-source machine learning library, which runs entirely in your browser. This means you can build image recognition prototypes for object detection projects, color-based sorting systems, or emotion recognition applications without any server-side processing.

Audio Classification

Teachable Machine also enables you to create models that classify sounds. By recording short audio samples through your microphone or uploading existing audio files, you can train models to recognize speech commands, detect ambient sounds, or even distinguish between different types of music. This feature opens up exciting possibilities for voice-controlled projects, sound-activated installations, and environmental monitoring applications.

Pose Recognition

The pose recognition feature lets you teach models to classify body positions captured through your webcam. Based on pose estimation algorithms, this capability enables gesture-controlled interfaces, interactive games that respond to your movements, fitness applications that provide real-time exercise guidance, and accessibility solutions that use body language as input.

One-Click Model Training

Perhaps the most user-friendly aspect of Teachable Machine is its streamlined training process. You don't need to understand feature extraction, hyperparameter tuning, or model architecture. Simply gather your samples, organize them into categories, click the train button, and the system handles all the complex mathematics behind the scenes. The training process is remarkably quick, often completing in seconds or minutes depending on your data size.

Multiple Export Formats

Once you've trained a model, Teachable Machine gives you flexibility in how you use it. You can export models in TensorFlow.js format for embedding in websites, TensorFlow Lite format for mobile applications, or Keras format for Python development projects. This interoperability with the broader TensorFlow ecosystem means your learning prototypes can evolve into production applications.

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Who is Using Teachable Machine

Teachable Machine serves a remarkably diverse community of users, from kindergarten students to professional researchers. Understanding who else uses this tool can help you envision your own projects and find relevant learning resources.

K-12 AI Education

Perhaps the largest community of users is in elementary and secondary education. Teachable Machine has become a cornerstone tool for introducing artificial intelligence concepts to young learners. When combined with curriculum resources from partners like Ready AI, it enables students to create their own machine learning models and directly experience how classifiers work. Rather than learning abstract mathematical concepts, students build intuition by teaching computers to recognize their own drawings, voices, and gestures. This hands-on approach has proven incredibly effective for making AI education accessible and engaging.

AI Ethics Education

Educators at MIT Media Lab have developed specialized courses using Teachable Machine to teach AI ethics. Students learn about algorithmic bias by collecting their own datasets and observing how data imbalances affect model behavior. For example, a class might discover that a model trained primarily on samples from one group performs poorly on others—creating a powerful, tangible lesson about the importance of diverse training data. These ethics-focused lessons have been recognized as valuable Common Core-aligned content for K-8 students.

Maker and Hardware Integration

The maker community has embraced Teachable Machine as a bridge between software intelligence and physical projects. By exporting models and connecting them to Arduino boards or Raspberry Pi computers, creators have built projects like Tiny Sorter—a cardboard robot that uses machine learning to sort objects by color or type. These hardware integrations demonstrate how edge AI can bring intelligence to physical devices, opening doors for robotics projects, smart home applications, and interactive installations.

Assistive Technology Development

Perhaps the most inspiring applications come from developers creating tools for people with disabilities. Steve Saling, who has ALS, uses a Teachable Machine-powered system that recognizes facial gestures and triggers corresponding sounds or actions. This allows individuals with limited mobility to communicate and control their environment using intuitive body movements. Such projects demonstrate how accessible AI tools can empower marginalized communities and create meaningful independence.

Game and Interaction Design

Game developers and interactive artists use Teachable Machine to experiment with novel control schemes. The popular Teachable Snake project, for instance, transforms a simple paper drawing into a game controller—players tilt and move their paper to control the snake, turning everyday objects into interactive game elements. These creative applications showcase how machine learning can enable entirely new forms of human-computer interaction.

Rapid Prototyping

Developers working on machine learning products use Teachable Machine to quickly validate concepts before investing in full engineering implementations. The ability to go from idea to working prototype in minutes dramatically accelerates the initial stages of AI product development. Teams can test whether their data collection approach works, validate that the classification task is feasible, and demonstrate proof-of-concept to stakeholders before committing significant development resources.

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Quick Start: Creating Your First Model

Ready to experience machine learning hands-on? Let's walk through the complete process of creating your first model in Teachable Machine. You'll be surprised how quickly you can go from complete beginner to having a working machine learning model.

Step 1: Access the Tool

There's nothing to install or configure. Simply open your web browser and navigate to teachablemachine.withgoogle.com. The tool works best in Chrome but is compatible with Firefox, Safari, and other modern browsers. You don't need to create an account or provide any personal information to start building.

Step 2: Choose Your Data Type

On the main page, you'll see three options: Image, Audio, and Pose. Select the type of data you want to use for training. For your first project, image classification is often the most intuitive—it's easy to understand and provides immediate visual feedback.

Step 3: Collect Training Data

Now it's time to teach your model what you want it to learn. Click the "Webcam" button to start capturing images directly, or upload files from your computer. Create at least two categories (classes) for your classifier—for example, "smiling" versus "not smiling," or "thumbs up" versus "thumbs down." Aim for 20-50 samples per category for basic projects; more samples generally improve accuracy.

Step 4: Train Your Model

Once you've collected your samples, click the "Train" button. The training happens entirely in your browser—the progress indicator shows you how it's progressing. For simple projects with modest datasets, training typically completes in under a minute. You don't need to configure any settings; the system automatically optimizes for your specific data.

Step 5: Test Your Model

After training completes, use the "Preview" section to test your model in real-time. Point your webcam at new examples and observe how the model classifies them. You'll immediately see confidence scores for each category, giving you insight into how well your model generalizes.

Step 6: Export and Use

If satisfied with performance, you can export your model. Choose TensorFlow.js for web projects, TensorFlow Lite for mobile apps, or Keras for Python development. The export includes documentation on how to integrate your model into other applications.

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Technical Features and Architecture

Understanding the technical foundation of Teachable Machine helps you appreciate why it works so well and what limitations to expect. This knowledge becomes particularly valuable when you're debugging issues or planning more advanced projects.

Browser-Based Architecture

The most distinctive technical characteristic of Teachable Machine is its entirely browser-based execution model. Unlike traditional machine learning workflows that require server-side processing, Teachable Machine performs all computation directly in your web browser. This architectural decision stems from Google's investment in TensorFlow.js, a JavaScript library that brings machine learning capabilities to client-side applications. The implications are significant: no data is sent to external servers during training or inference, latency is minimal since there's no network round-trip, and the tool works even without internet connectivity once loaded.

TensorFlow.js Foundation

Teachable Machine builds on TensorFlow.js, Google's open-source machine learning framework for the web. This technology enables efficient execution of neural networks directly in browsers, leveraging WebGL acceleration when available. The same underlying technology powers machine learning features in products like Google Photos and Google Assistant, giving you access to production-grade AI capabilities in an educational package.

Privacy-First Design

The optional "entirely device-based" mode represents a deliberate design choice prioritizing user privacy. When enabled, webcam and microphone data never leaves your local machine—the browser processes everything locally. This makes Teachable Machine suitable for educational environments with strict data policies, privacy-conscious individuals, and scenarios where network connectivity is unreliable. Understanding this option helps you make informed decisions about when to use the device-only mode versus the standard cloud-assisted configuration.

Multi-Format Export Capabilities

The export functionality demonstrates Teachable Machine's role as a bridge between education and production. By supporting TensorFlow.js for web deployment, TensorFlow Lite for mobile and edge devices, and Keras for Python development, the tool enables your learning prototypes to evolve into real applications. This compatibility with the broader TensorFlow ecosystem means skills learned in Teachable Machine transfer directly to professional machine learning workflows.

Cross-Platform Compatibility

Because Teachable Machine runs entirely in the browser, it works across operating systems without modification. Whether you're using Windows, macOS, Linux, or even Chrome OS, the experience remains consistent. This cross-platform nature makes it ideal for educational environments where students may use different devices.

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Frequently Asked Questions