TMIC is an App Inventor extension for the deployment of ML models for image classification developed with Google Teachable Machine in educational settings. Google Teachable Machine, is an intuitive visual tool that provides workflow-oriented support for the development of ML models for image classification. Aiming at the usage of models developed with Google Teachable Machine, the extension TMIC enables the deployment of the trained models exported as TensorFlow.js to Google Cloud as part of App Inventor, one of the most popular block-based programming environments for teaching computing in K-12. The extension was created with the App Inventor extension framework based on the extension PIC and is available under the BSD 3 license. It can be used for teaching ML in K-12, in introductory courses in higher education or by anyone interested in creating intelligent apps with image classification. The extension TMIC is being developed by the initiative Computa\c{c}\~ao na Escola of the Department of Informatics and Statistics at the Federal University of Santa Catarina/Brazil as part of a research effort aiming at introducing AI education in K-12.

Build, train, test and deploy AI models to classify fashion items using Google Teachable Machine. Build, train and deploy advanced AI to detect Diabetic Retinopathy disease using DataRobot AI. Leverage the power of AI to solve regression tasks and predict used car prices using DataRobot AI. Evaluate trained AI models using various KPIs such as confusion matrix, classification accuracy, and error rate. Understand the theory and intuition behind Residual Neural Networks (ResNets), a state-of-the-art deep NNs that are widely adopted in several industries.

Build, train, test and deploy AI models to classify fashion items using Google Teachable Machine. Build, train and deploy advanced AI to detect Diabetic Retinopathy disease using DataRobot AI. Leverage the power of AI to solve regression tasks and predict used car prices using DataRobot AI. Evaluate trained AI models using various KPIs such as confusion matrix, classification accuracy, and error rate. Understand the theory and intuition behind Residual Neural Networks (ResNets), a state-of-the-art deep NNs that are widely adopted in several industries.

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AI is a general field that encompasses machine learning and deep learning. The history of artificial intelligence in its modern sense begins in the 1950s, with the works of Alan Turing and the Dartmouth workshop, which brought together the first enthusiasts of this field and in which the basic principles of the science of AI were formulated. Further, this industry experienced several cycles of a surge of interest and subsequent recessions (the so-called "AI winters"), in order to become one of the key areas of world science today. However, there are several examples and applications of artificial intelligence in use today, a large community of developers is still wondering how or from where to start developing AI-driven applications. So this article may be a kick start for those who are eager to start developing AI or ML-driven applications.