Machine Learning for Flutter The Complete 2023 Guide
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• Machine Learning for Flutter - The Complete 2024 Guide
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Welcome to the Machine Learning use in Flutter, The complete guide course.
Covering all the fundamental concepts of using ML models inside Flutter applications, this is the most comprehensive and only Flutter ML course available online.
We built this course over months, perfecting the curriculum, and covering everything that will help you learn to use Machine Learning models inside flutter applications. This course will teach you to build powerful ML-based applications in Flutter for Android and IOS devices.
The important thing is you don't need to know background working knowledge of Machine learning and computer vision to use ML models inside Flutter and train them.
The course is mainly divided into three major sections
Firebase ML Kit
Pretrained TensorFlow lite models
Training image classification models
In the first section, we will learn the use of Firebase ML Kit inside the Flutter applications for common use cases like
Image Labeling
Barcode Scanning
Text Recognition
Face Detection
So we will explore these features one by one and build Flutter applications. For each of the features of the ML Kit, we will build two applications. In the first application, we are gonna use the images taken from the gallery or camera, and in the second application, we are gonna use the live camera footage with the ML model. So you apart from simple ML-based applications you will also be able to build real-time face detection and image labeling application in Flutter using the live camera footage.
After covering the Google ML Kit, In the second section of this course, you will learn about using Tensorflow lite models inside Flutter. Tensorflow Lite is a standard format for running ML models on mobile devices. So in this section, you will learn the use of pretrained powered ML models inside Flutter for building
Image Classification (ImageNet V2 model)
Object Detection (MobileNet model, Tiny Yolo model)
Pose Estimation (PostNet model)
Image Segmentation (Deeplab model)
applications. So not only you will learn to use these models with images but you will also learn to use them with frames of camera footage to build real-time applications.
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