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所在平台: Udemy |
课程主页: https://www.udemy.com/course/machine-learning-for-flutter-the-complete-2023-guide/
课程评论：没有评论
课程名称：Flutter中的机器学习应用 - 2025 Flutter ML指南 课程概述： 欢迎参加《Flutter中的机器学习：完整指南》。本课程将带您深入掌握将机器学习模型集成到Flutter应用程序中的技术，成为网络上最全面的Google Flutter ML课程之一。无论您是初学者还是经验丰富的开发者，都能够快速上手，无需任何机器学习或计算机视觉的先前知识。 您将学习： 1. 利用现有的机器学习模型：将预训练的TensorFlow Lite模型和Firebase ML Kit集成到您的Flutter应用（适用于Android和iOS）中。 2. 训练自定义机器学习模型：学习如何为图像分类和物体检测训练自己的机器学习模型，无需深厚的背景知识。 3. 计算机视觉技术：实现高级计算机视觉功能，如图像分类、物体检测、图像分割、条形码扫描、姿态估计等。 4. 实时应用程序：构建处理实时摄像头画面的应用程序，执行实时机器学习任务，包括文本识别、面部检测和图像标签。 5. 综合Flutter项目：创建20多个完整的Flutter应用，展示您处理各种机器学习任务和计算机视觉模型的能力。 涵盖的机器学习特性： - 图像分类：从图库和实时摄像头画面分类图像。 - 物体检测：在图像和实时摄像头画面中检测物体。 - 图像分割：通过分割图像使其透明。 - 条形码扫描：扫描条形码和二维码。 - 姿态估计：检测人体关节。 - 文本识别：识别图像中的文本。 - 文本翻译：在不同语言之间翻译文本。 - 面部检测：检测面部、面部特征和表情。 - 智能回复：生成聊天应用中的智能回复建议。 - 数字墨水识别：识别手写文本。 - 语言识别：识别给定文本的语言。 - 实体提取：从文本中提取不同实体。 课程亮点： - 关键库介绍：包括图像选择器和摄像头的使用。 - Firebase ML Kit集成：使用图像标记、条形码扫描、文本识别、面部检测等功能构建应用程序。 - TensorFlow Lite模型：实现预训练的图像分类和物体检测模型，使用MobileNet和EfficientNet等模型创建实时应用。 - 自定义模型训练：收集和准备数据集，训练图像分类和物体检测模型，将模型转换为TensorFlow Lite格式以供Flutter应用使用。 适合人群： - 初学者：新手Flutter和移动应用开发者。 - 中级开发者：希望集成高级机器学习特性的Flutter开发者。 - 经验丰富的开发者：希望通过自定义机器学习和计算机视觉模型增强应用的开发者。 - 技术爱好者：对在移动应用中探索人工智能和机器学习感兴趣的任何人。 为什么选择这个课程？ - 综合内容：包含20多款完整的Flutter应用。 - 专家指导：课程由拥有超过6年经验的Muhammad Hamza Asif主讲，并拥有超过60,000名学员的社区支持。 - 完整信心：提供30天无条件退款保证。 立即加入，利用强大的机器学习能力提升您的Flutter开发技能。点击“立即购买”开始您在AI驱动的Flutter应用世界中的旅程！
Welcome to Machine Learning in Flutter: The Complete GuideMaster 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:Image Classification: Classify images from the gallery and live camera footage.Object Detection: Detect objects in images and real-time camera frames.Image Segmentation: Make images transparent by segmenting them.Barcode Scanning: Scan barcodes and QR codes.Pose Estimation: Detect human body joints.Text Recognition: Recognize text in images.Text Translation: Translate text between different languages.Face Detection: Detect faces, facial landmarks, and expressions.Smart Reply: Generate smart reply suggestions in chat applications.Digital Ink Recognition: Recognize handwritten text.Language Identification: Identify the language of a given text.Entity Extraction: Extract different entities from text.Course Highlights:Introduction to Key Libraries:Image Picker: Choose images from the gallery or capture with the camera.Camera: Access live camera footage frame by frame.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.TensorFlow Lite Models:Implement pre-trained models for image classification and object detection.Create real-time applications using models like MobileNet and EfficientNet.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 20 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!
