Liner AI: Best Machine Learning Without Code AI Tool

This discussion delves into the capabilities and features of Liner ai, a no-code platform designed to facilitate machine learning (ML) and artificial intelligence (AI) tasks, especially for those new to the field.

Liner ai is an intriguing and essential tool, particularly for lecturers who suddenly find themselves teaching machine learning or AI courses. It is especially useful for those who are confused about creating simple, non-technical material for undergraduate courses. In the past, educators often relied on tools like wk for an easier approach to running something. However, now there’s Liner ai, an end-to-end machine learning application designed to simplify the learning process.

Machine Learning Without code

Liner ai emerges as a significant tool, particularly for educators who unexpectedly find themselves teaching machine learning or AI courses. It’s tailored for those who might feel overwhelmed when creating foundational material for undergraduate health or non-technical courses that incorporate machine learning elements.

Transitioning from Weka to Liner ai

Traditionally, platforms like Weka have been favored for their user-friendly approach to running ML tasks. However, Liner.ai offers an end-to-end machine learning application that leverages transfer learning and various models. This positions it as a comprehensive AI tool suitable for a range of applications.

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Features and Offerings – No code ML

Liner ai offers a variety of features that make it a powerful tool for educators and beginners in AI. It supports transfer learning and utilizes various models for training, making it a robust learning tool. The application covers a wide range of functionalities, from image recognition and segmentation to classification tasks, including NLP (Natural language processing). Although the NLP classification feature is not fully shown in the example project, the tool provides datasets such as samples of dogs, cats, monkey species, flowers, and even number plate recognition. These capabilities make Liner.ai a versatile tool for a range of machine learning tasks.

Sample Projects and Datasets

Liner ai boasts a plethora of sample projects. These range from image recognition, segmentation, and classification to Natural Language Processing (NLP) tasks. While NLP classifications aren’t prominently showcased in the example projects, the platform provides datasets that include:

• Dog and cat images
• Monkey species data
• Flower datasets
• Tasks like reading car number plates and road signs

Note: The NLP section might still be in experimental stages, hence its limited visibility.

Tutorials and Documentation

While Liner ai offers tutorials, directing users to YouTube for guidance, its documentation is currently sparse, reflecting its nascent stage in development.

Platform Compatibility

The tool is available for:

• Windows – Download Now
• Mac (both Intel and Silicon versions)
• Download Intel Version | Download Silicon Version
• Linux – Coming Soon

Linux users might have to wait a bit longer, but given that Liner ai utilizes Electron.js, a Linux version is likely on the horizon.

Hands-On with Liner ai

Liner ai is still relatively new, so its documentation is sparse. However, the application is available for download on Windows and Mac, with support for both Intel and Silicon processors. A Linux version is expected soon. The tool uses Electron.js, ensuring cross-platform compatibility. Once downloaded and installed, users can easily start exploring the various modes Liner.ai offers, such as image classification and audio classification.

Installation and Interface

After downloading and installing Liner ai, users might notice some quirks, such as certain modes not appearing immediately. However, by navigating through options like “Request Liner,” functionalities like image and audio classification become accessible.

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Image Classification Example

Dataset Preparation:

Using a melanoma dataset stored on an external SSD.

Data Import:

The platform allows manual data imports, categorizing data (e.g., 826 benign samples and 374 malignant samples).

Model Selection: Users can choose from models like EfficientNet, MobileNet, aSqueezeNet. Past research has combined models like SqueezeNet with tabular variables for enhanced accuracy.

Training Process:

The training process in Liner ai is straightforward. For example, when working with a melanoma dataset, the tool allows you to import data manually and then proceed with training. The default model used is EfficientNet, with other models like MobileNet and ZipNet also available. The training process typically takes about 5-7 minutes on a MacBook Air with 8GB RAM. The validation accuracy achieved is around 90%, which is quite satisfactory given the small dataset used.

Results:

Validation Accuracy: Around 90%.

Cross-Class Analysis: Comparable performance across classes, though some variance in recall scores.

Testing: Users can test the model with external data, such as images captured via a camera.

Exporting the Model:

One of the most exciting features of Liner.ai is its ability to export models to a simple Python web application. The export can be done using Flask or Django, making it easy to integrate the models into larger applications. The export files are relatively small, making them easy to manage and deploy. Although the tool is still evolving, its potential for making AI more accessible to non-coders is evident.
Options to export as a simple Python web application using frameworks like Flask or Django.
Exported models are compact (e.g., 16 MB) and can be served locally for testing.

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NLP Classification Example

1. Dataset: Sample data sourced from a factory, encompassing seven classes.
2. Model Selection: Options include DistilBERT and TinyBERT. DistilBERT, a distilled version of BERT, offers reduced size without significant performance compromise.
3. Training:
   • Quick feature extraction and training, often completed in mere seconds.
   • Achieved an accuracy of approximately 87% across seven classes.
4. Model Inspection:
   • Ability to export models to Keras for visualization of network structures.
5. Deployment:
   • Similar to image classification, NLP models can be exported and tested using local web applications.

Alternatives and Comparisons

While Liner ai offers a robust platform for beginners, other tools like Weka and KNIME have been longstanding in the ML community. Additionally, platforms like Jellyfish and Orange provide alternative approaches to no-code machine learning, incorporating algorithms like XGBoost and LightGBM.

Conclusion

Liner ai stands out as a promising tool for those seeking an introduction to machine learning without diving deep into coding. Its user-friendly interface, combined with powerful models and quick training times, makes it a valuable asset for educators and beginners alike. As the platform continues to develop, features like comprehensive documentation and broader compatibility are anticipated.

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Liner ai is an alternative AI recognition software designed for beginners who prefer not to write code. It provides a user-friendly interface that allows users to click and run machine learning models with minimal effort. While the tool is still in its early stages, it offers a promising alternative to more complex machine learning tools, making AI more accessible to a broader audience.

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