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Machine Learning in Flutter: Complete Guide
This course teaches learners how to integrate Machine Learning with Flutter, enabling the creation of smart, responsive apps with personalized user experiences. Unlock new potential in mobile development with ML and Flutter!
Course Highlights
Learn the smart way of using machine learning in Flutter to build smart android and iOS applications with the Machine Learning in Flutter: Complete Guide course.
Flutter is a software development kit for developing beautiful applications from a single codebase. Moreover, if you can infuse machine learning within Flutter, you can craft wonders. This complete guide aims to provide you with the fundamental knowledge and skills required to use Machine Learning in Flutter to equip you with this phenomenal skill.
This complete guide will give you a solid foundation of the Firebase ML kit as well as a detailed idea about the image picker library and camera package setup for Flutter. You will explore a broad range of flutter widgets like image labelling, face detection, pose estimation and so on.
Enrol in this Machine Learning in Flutter: Complete Guide course today and build high-quality and functionally-rich apps using machine Learning in Flutter.
Learning outcome
- Familiar yourself with the recent development and updates of the relevant industry
- Know how to use your theoretical knowledge to adapt in any working environment
- Get help from our expert tutors anytime you need
- Access to course contents that are designed and prepared by industry professionals
- Study at your convenient time and from wherever you want
Course media
Why should I take this course?
- Affordable premium-quality E-learning content, you can learn at your own pace.
- You will receive a completion certificate upon completing the course.
- Internationally recognized Accredited Qualification will boost up your resume.
- You will learn the researched and proven approach adopted by successful people to transform their careers.
- You will be able to incorporate various techniques successfully and understand your customers better.
Requirements
- No formal qualifications required, anyone from any academic background can take this course.
- Access to a computer or digital device with internet connectivity.
Course Curriculum
Course Curriculum
| Unit 01: Introduction | |||
| Module 01: Course Curriculum | 00:02:00 | ||
| Unit 02: Image Picker and Camera Libraries | |||
| Module 01: Image Picker Library for Flutter App Development | 00:13:00 | ||
| Module 02: Flutter Image Picker Application Testing | 00:01:00 | ||
| Module 03: Camera Package Setup for Flutter | 00:04:00 | ||
| Module 04: Flutter Camera Package Code | 00:08:00 | ||
| Unit 03: Firebase ML Kit | |||
| Module 01: Firebase ML kit section Introduction | 00:01:00 | ||
| Module 02: Firebase ML Kit introduction | 00:02:00 | ||
| Unit 04: Image Labeling using ML Kit | |||
| Module 01: Flutter Image Labeling Section Introduction | 00:02:00 | ||
| Module 02: Importing Starter code for image labeling | 00:01:00 | ||
| Module 03: Image labeling starter code explanation | 00:06:00 | ||
| Module 04: Creating firebase project for image labeling | 00:06:00 | ||
| Module 05: Adding Firebase ML Vision library in Flutter Application | 00:10:00 | ||
| Module 06: Testing Firebase Image labeling application | 00:01:00 | ||
| Module 07: Importing Image Labeling live feed application starter code | 00:03:00 | ||
| Module 08: Flutter Camera Package Code | 00:06:00 | ||
| Module 09: Flutter Image Labeling live feed application code | 00:08:00 | ||
| Module 10: Flutter Image labeling live feed application testing | 00:01:00 | ||
| Unit 05: Section Barcode Scanning | |||
| Module 01: Flutter Barcode Scanning Section Introduction | 00:02:00 | ||
| Module 02: Importing Starter code for Flutter Barcode Scanning | 00:03:00 | ||
| Module 03: Flutter Barcode Scanning code | 00:11:00 | ||
| Module 04: Flutter Barcode Scanning Application Testing | 00:01:00 | ||
| Module 05: Flutter Barcode Scanning Live Feed Application code | 00:08:00 | ||
| Module 06: Flutter Barcode Scanning Live feed Application Testing | 00:01:00 | ||
| Unit 06: Section Text Recognition | |||
| Module 01: Flutter Text Recognition Section Introduction | 00:01:00 | ||
| Module 02: Importing Starter code for Flutter Text Recognition | 00:03:00 | ||
| Module 03: Writing Flutter Text Recognition Code | 00:09:00 | ||
| Module 04: Testing Flutter Text Recognition Application | 00:01:00 | ||
| Unit 07: Section Face Detection | |||
| Module 01: Flutter Face Detection Section Introduction | 00:02:00 | ||
| Module 02: Flutter Face Detection Application Flow | 00:01:00 | ||
| Module 03: Flutter Face Detection code | 00:06:00 | ||
| Module 04: Flutter drawing rectangles around detected faces | 00:05:00 | ||
| Unit 08: Pretrained Tensorflow lite models | |||
| Module 01: Pretrained Tensorflow lite models Section Introduction | 00:02:00 | ||
| Unit 09: Section Image Classification | |||
| Module 01: Flutter Image classification Section introduction | 00:02:00 | ||
| Module 02: Importing Starter code for Flutter Image classification application | 00:03:00 | ||
| Module 03: Starter code explanation for Flutter Image classification | 00:06:00 | ||
| Module 04: Writing flutter image classification code | 00:13:00 | ||
| Module 05: Testing flutter image classification application | 00:02:00 | ||
| Module 06: Importing Flutter live feed Image classification application starter code | 00:03:00 | ||
| Module 07: Starter code explanation of Flutter Live feed Image classification application | 00:05:00 | ||
| Module 08: Writing Flutter Image classification code | 00:11:00 | ||
| Module 09: Testing live feed image classification flutter application | 00:01:00 | ||
| Unit 10: Section object detection | |||
| Module 01: Flutter Object detection section introduction | 00:02:00 | ||
| Module 02: Importing Application code object detection flutter | 00:05:00 | ||
| Module 03: Flutter Object detection code | 00:13:00 | ||
| Module 04: Flutter Drawing Rectangles around detected objects | 00:04:00 | ||
| Module 05: Importing the code for live feed object detection flutter application | 00:02:00 | ||
| Module 06: Testing object detection live feed flutter application | 00:01:00 | ||
| Module 07: Flutter Live feed object detection application code | 00:10:00 | ||
| Unit 11: Section human pose estimation | |||
| Module 01: Flutter Pose estimation section introduction | 00:02:00 | ||
| Module 02: Importing Flutter Pose estimation Application code | 00:04:00 | ||
| Module 03: Flutter Pose estimation code | 00:10:00 | ||
| Module 04: Importing pose estimation live feed flutter application code | 00:02:00 | ||
| Module 05: Flutter Live feed pose estimation application demo | 00:01:00 | ||
| Module 06: Using PoseNet model for Flutter Live feed pose estimation application | 00:08:00 | ||
| Unit 12: Image segmentation section | |||
| Module 01: Flutter Image Segmentation Section Introduction | 00:02:00 | ||
| Module 02: Importing Flutter Image Segmentation Application code | 00:03:00 | ||
| Module 03: Flutter using DeepLab model for image segmentation | 00:09:00 | ||
| Unit 13: Section Training Image Classification Models | |||
| Module 01: Section Introduction | 00:02:00 | ||
| Module 02: Machine Learning and Image classification | 00:02:00 | ||
| Unit 14: Dog Breed Classification | |||
| Module 01: Flutter getting the dataset for model training | 00:05:00 | ||
| Module 02: Flutter Training the model | 00:06:00 | ||
| Module 03: Flutter Dog Breed Classification Application | 00:18:00 | ||
| Module 04: Flutter Live feed dog breed classification application | 00:03:00 | ||
| Module 05: Testing live feed dog breed classification application | 00:01:00 | ||
| Unit 15: Fruits Recognition using Transfer Learning | |||
| Module 01: Transfer learning introduction | 00:02:00 | ||
| Module 02: Flutter getting the dataset for model training | 00:05:00 | ||
| Module 03: Flutter Training fruit recognition model | 00:09:00 | ||
| Module 04: Flutter Testing Live feed fruits recognition application | 00:01:00 | ||
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1 Year Access 
66 Students 
5 hours, 9 minutes