Machine Learning use in Flutter – The Complete 2025 Guide | Udemy

Welcome to Machine Learning in Flutter: The Complete 2025 Guide

Master the integration of machine learning models in your Flutter applications with the most comprehensive Google Flutter ML course available online.

No prior knowledge of machine learning or computer vision required! Whether you are a beginner or an experienced developer, this course will guide you through using and training machine learning models in Flutter (Android & iOS) applications.

What You Will Learn:

• Utilize Existing ML Models: Learn to integrate pre-trained TensorFlow Lite models and Firebase ML Kit into your Flutter applications for both Android and iOS.
• Train Custom ML Models: Discover how to train your own machine learning models for image classification and object detection without needing extensive background knowledge.
• Computer Vision Techniques: Implement advanced computer vision features like image classification, object detection, image segmentation, barcode scanning, pose estimation, and more.
• Real-time Applications: Build applications that process live camera footage for real-time ML tasks, including text recognition, face detection, and image labeling.
• Comprehensive Flutter Projects: Create over 20 complete Flutter applications, showcasing your ability to handle various ML tasks and computer vision models.

Machine Learning Features Covered:

1. Image Classification: Classify images from the gallery and live camera footage.
2. Object Detection: Detect objects in images and real-time camera frames.
3. Image Segmentation: Make images transparent by segmenting them.
4. Barcode Scanning: Scan barcodes and QR codes.
5. Pose Estimation: Detect human body joints.
6. Text Recognition: Recognize text in images.
7. Text Translation: Translate text between different languages.
8. Face Detection: Detect faces, facial landmarks, and expressions.
9. Smart Reply: Generate smart reply suggestions in chat applications.
10. Digital Ink Recognition: Recognize handwritten text.
11. Language Identification: Identify the language of a given text.
12. Entity Extraction: Extract different entities from text.

Course Highlights:

1. Introduction to Key Libraries:
   • Image Picker: Choose images from the gallery or capture with the camera.
   • Camera: Access live camera footage frame by frame.
2. Firebase ML Kit Integration:
   • Build applications using features like image labeling, barcode scanning, text recognition, face detection, and more with both static images and live camera footage.
3. TensorFlow Lite Models:
   • Implement pre-trained models for image classification and object detection.
   • Create real-time applications using models like MobileNet and EfficientNet.
4. Training Custom Models:
   • Gather and prepare datasets.
   • Train image classification and object detection models.
   • Convert models to TensorFlow Lite format for use in Flutter apps.

Who This Course is For:

• Beginners: Those new to Flutter and mobile app development.
• Intermediate Developers: Flutter developers looking to integrate advanced ML features.
• Experienced Developers: Developers seeking to enhance their apps with custom machine learning and computer vision models.
• Tech Enthusiasts: Anyone interested in exploring AI and ML within mobile applications.

Why Enroll?

• Comprehensive Content: Over 22 fully-fledged Flutter applications.
• Expert Instruction: Led by Muhammad Hamza Asif, with 6+ years of experience and a community of 60,000+ students.
• Complete Confidence: 30-day money-back guarantee from Udemy.

Join now and transform your Flutter development skills with powerful machine learning capabilities. Click “Buy Now” to start your journey in the world of AI-driven Flutter applications!