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Machine Learning for Flutter The Complete Guide – Flutter ML

TensorFlow lite & Firebase ML Kit use in Flutter , Train ML Models for Flutter ,15+ Flutter Android and IOS Applications
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Content

Platform: Udemy
Video: 6h 24m
Language: English
Next start: On Demand

Table of contents

Description

Important Notice: Firebase ML Kit section of course is updated with the new package.
Welcome to the Machine Learning use in Flutter The Complete Guide – Flutter ML course.
Covering all the fundamental concepts of using ML models inside Flutter applications, this is the most comprehensive Google Flutter ML course available online.
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 2.0 ( Dart ) and train your custom machine learning models.
Starting from a very simple example course will teach you to use advanced ML models in your Flutter ( Android & IOS ) applications. So after completing this course you will be able to use both simple and advanced Tensorflow lite models along with a Firebase ML Kit in your Flutter ( Android & IOS ) applications.
What we will cover in this course?
•Learning the use of existing machine learning models in Flutter (Android and IOS) applications
•Learn to train your own custom machine learning models and build Flutter applications
•Choosing images from gallery ad capturing images using camera  in Flutter
•Displaying live camera footage and fetching frames of live camera footage in Flutter
•Image classification with images and live camera footage in Flutter (Android and IOS)
•Object Detection with Images and Live Camera footage in Flutter (Android and IOS)
•Image Segmentation to make images transparent in Flutter (Android and IOS)
•Use of regression models in Flutter (Android and IOS)
•Image Labeling Flutter to recognize different things
•Barcode Scanning in Flutter to scan barcodes and QR codes
•Pose Estimation in Flutter to detect human body joints
•Text Recognition in Flutter to recognize text in images
•Text Translation in Flutter to translate between different languages
•Face Detection in Flutter to detect faces, facial landmarks, and facial expressions
•Training image classification models for Flutter (Android and IOS) applications
•Retraining existing machine learning models with transfer learning for Flutter (Android and IOS) applications
•Using our custom machine learning models in Flutter (Android and IOS) applications
Course structure
We will start by learning about two important libraries
•Image Picker: to choose images from the gallery or capture images using the camera in Flutter
•Camera: to get live footage from the camera frame by frame in Flutter
So later we can use a computer vision model with both images and live camera footage in Flutter.
Then we will learn about the Firebase ML kit and the features it provides. We will explore the features of the Firebase ML Kit and build two flutter applications using each feature.
The flutter applications we will build in that section are
•Image labeling Flutter application using images of gallery and camera
•Image labeling Flutter application using live footage from the camera
•Barcode Scanning Flutter application using images of gallery and camera
•Barcode Scanning Flutter application using live footage from the camera
•Text Recognition Flutter application using images of gallery and camera
•Face Detection Flutter application using images of gallery and camera
•Face Detection Flutter application using live footage from the camera
After learning the use of Firebase ML Kit inside Google Flutter (Android& IOS) applications we will learn the use of popular pre-trained TensorFlow lite models inside Google Flutter applications. So we explore some popular machine learning models and build the following Google Flutter applications in this section
•Image classification Flutter application using images of gallery and camera
•Image classification Flutter application using live footage from the camera
•Object detection Flutter application using images of gallery and camera
•Object detection Flutter application using live footage from the camera
•Human pose estimation Flutter application using images of gallery and camera
•Human pose estimation Flutter application using live footage from the camera
•Image Segmentation Flutter application using images of gallery and camera
•Image Segmentation Flutter application using live footage from the camera
After that, we will learn to use Regression models in Google Flutter and build a couple of applications including
•Basic Regression Flutter Application for Android and IOS
•Fuel Efficiency predictor for vehicles in Flutter for Android and IOS
After learning the use of pre-trained machine learning models using Firebase ML Kit and Tensorflow lite models inside Flutter ( Dart ) we will learn to train our own Image classification models without knowing any background knowledge of Machine Learning. So we will learn to
•Gether and arrange the data set for the machine learning model training
•Training Machine learning some platforms with just a few clicks
So in that section, we will
•Train a dog breed classification model for Flutter
•Build a Flutter ( Android & IOS ) application to recognize different breeds of dogs
•Train Fruit recognition model using Transfer learning
•Building a Flutter ( Android & IOS ) application to recognize different fruits

So the course is mainly divided  into three major sections
•Firebase ML Kit for Flutter
•Pretrained TensorFlow lite models for Flutter
•Training image classification models for Flutter
In the first section, we will learn the use of Firebase ML Kit inside the Flutter dart applications for common use cases like
•Image Labeling in Flutter with Images and live camera footage
•Barcode Scanning in Flutter with Images and live camera footage
•Text Recognition in Flutter with Images and live camera footage
•Face Detection in Flutter with Images and live camera footage
So we will explore these features one by one and build Flutter applications. For each of the features of the Firebase 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 Firebase 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 Google Flutter dart using the live camera footage. So after completing this section you will have a complete grip on Google Firebase ML Kit and also you will be able to use upcoming features of Firebase ML Kit for Google Flutter ( Dart ).
After covering the Google Firebase ML Kit, In the second section of this course, you will learn about using Tensorflow lite models inside Google Flutter ( Dart ). 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 Google Flutter dart for building
•Image Classification Flutter ( ImageNet V2 model )
•Object Detection  Flutter ( MobileNet model, Tiny YOLO model)
•Pose Estimation  Flutter ( PostNet model )
•Image Segmentation  Flutter ( 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 flutter applications.
So after learning the use of Machine Learning models inside Flutter dart using two different approaches in the third section of this course you will learn to train your own Machine Learning models without any background knowledge of machine learning. So in that section, we will explore some platforms that enable us to train machine learning models for mobile devices with just a few clicks. So in the third section, you will learn to
•Collect and arrange the dataset for model training
•Training the Machine Learning models from scratch using Teachable-Machine
•Retraining existing models using Transfer Learning
•Using those trained models inside Google Flutter dart Applications
So we will train the models to recognize different breeds of dogs and to recognize different fruits and then build Google Flutter Applications using those models for android and IOS.
By the end of this course, you will be able
•Use Firebase ML kit inside Google Flutter dart applications for Android and IOS
•Use pre-trained Tensorflow lite models inside Android & IOS application using Google Flutter dart
•Train your own Image classification models and build Flutter applications.
You’ll also have a portfolio of over 15 Flutter apps that you can show off to any potential employer.
Sign up today, and look forwards to:
•HD 1080p video content, everything you’ll ever need to succeed as a Google Flutter Machine Learning developer.
•Building over 15 fully-fledged flutter applications including ones that use Objet detection, Text Recognition, Pose estimation models, and much much more.
•All the knowledge you need to start building Machine Learning-based Flutter (Android or IOS) application you want
•$2000+ Source codes of 15 Applications.
REMEMBER… I’m so confident that you’ll love this course that we’re offering a FULL money-back guarantee for 30 days! So it’s a complete no-brainer, sign up today with ZERO risks and EVERYTHING to gain.
So what are you waiting for? Click the buy now button and join the world’s best Google Flutter ( Dart ) Machine Learning course.
Who this course is for:
•Beginner Flutter ( Dart ) developer with very little knowledge of mobile app development in Google Flutter
•Intermediate Flutter ( Dart ) developer wanted to build a powerful Machine Learning-based application in Google Flutter
•Experienced Flutter ( Dart ) developers wanted to use Machine Learning models inside their applications.
•Anyone who took a basic flutter ( Dart ) mobile app development course before (like Flutter ( Dart ) app development course by angela yu or other such courses).

You will learn

✓ Use of Machine learning models with images from gallery and camera in Flutter
✓ Use of Machine Learning models with live camera footage in Flutter
✓ Use of Tensorflow lite models in Flutter
✓ Training Machine Learning models for Flutter Applications
✓ How to integrate Firebase ML Kit in Flutter Applications
✓ Live Feed Image classification and Object Detection in Flutter
✓ Image Segmentation and Pose Estimation in Flutter
✓ Using Regression models in Flutter applications
✓ Image labeling and Barcode scanning in Flutter
✓ Text Recognition and Face Detection in Flutter
✓ Text Translation and Language identification in Flutter
✓ Building Machine learning based Realtime Flutter Applications
✓ Machine Learning models use in Flutter to build Smart Android and IOS Applications

Requirements

• Basic Knowledge of Mobile App development in Flutter
• Developer who knows to develop Hello World Application in Flutter

This course is for

• Anyone who took Basic Flutter course before
• Beginner Flutter Developer curious about Machine learning and computer vision use in Flutter
• Experienced Professional want to add ML models in their Flutter Applications
• App developer want to learn use of Machine learning in their Flutter Applications
• Intermediate Flutter developers looking to enhance their skillset
Android Developer | Instructor | Flutter Developer
Experienced Mobile Developer, specialized in Mobile Machine Learning using Tensorflow lite, ML Kit, and Google cloud vision API. Leading Android Machine learning instructor with over 50,000 students from 150 countries.
I am an enthusiastic developer with a strong programming background and possess great app development skills. I have developed a bunch of native and cross-platform apps in the past and satisfied all of my clients. It has been +4 years doing Mobile development and providing support for Android Applications. Empowering mobile Applications using Machine Learning and Computer vision is my core skill.
Powering Android Application with ML really fascinates me. So I learned Android development and then Machine Learning. I developed Android applications for several multinational organizations. Now I want to spread the knowledge I have. I’m always thinking about how to make difficult concepts easy to understand, what kind of projects would make a fun tutorial, and how I can help you succeed through my courses.
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Platform: Udemy
Video: 6h 24m
Language: English
Next start: On Demand

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