Machine learning is a scientific field that offers computers the ability to learn without being programmed directly. When many learners, students, engineers, and data scientists use machine learning to create diverse projects and goods, the application of machine learning is trendy. However, the development of machine learning models involves high device parameters, also the model training process can often vary from hours to days. Hence, low-end systems can not accommodate the training of successful machine learning models, or crucial system problems are likely to arise.
However, several Machine Learning environments are easily available on the internet that does not need any system specification or framework specifications and use cloud technology to train the model in the best possible time. Some of these open-source machine learning environments are Google Colaboratory, Kaggle Kernal , these are an excellent platform for deep learning and machine learning applications in the cloud. Both of them are google products and require the knowledge of data science in order to develop and train models using them. However, Google introduced a new open-source platform for training machine learning models that developers to code i.e Google’s Teachable Machine.
The Google Teachable Machine is an online open-source environment which is used to develop and train machine learning and deep learning supervised models without using any programming language.
Below is the step-by-step approach on how to use the Teachable Machine to develop and train machine learning models:
- Click on Get Started and choose whether to open an existing project or create a new project. In-order to create a new project we have three options i.e. Image Project, Audio Project, or Pose Project. Click on the Image project.
- After clicking on Image Project, the below web page will be displayed.
- Add a number of classes, rename them, and upload sample images for each class. The dataset we are going to use is COVID 19-Lung CT Scans.
- Then click on Advanced and adjust Epochs, Batch Size, and Learning Rate.
- Now click on Train Model, it will require some time to process. After the model is trained, click on under the hood to get accuracy and other details.
- You can test the model by uploading a sample input image.
- Click on Export Model to download the model or generate a shareable public link for the model.
In this way, one can easily develop machine learning and deep learning supervised models using Google’s Teachable Machine.
