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所在平台: Udemy |
课程主页: https://www.udemy.com/course/on-device-machine-learning-train-ml-models-and-deploy-in-mobile/
课程评论：没有评论
课程名称：2025年版移动应用上的机器学习与人工智能精通课程 课程概述：本课程旨在帮助学习者充分发挥机器学习（ML）和人工智能（AI）在移动开发中的潜力，内容为2025年准备就绪，并以实践为导向。无论您是初学者、移动开发者还是数据科学爱好者，您都将学习如何在设备上构建、训练和部署真正的机器学习模型，而无需依赖互联网或云服务。您将从机器学习和深度学习的基础知识入手，逐步创建在Android、iOS和Flutter上本地运行的智能移动应用，充分利用TensorFlow Lite的力量。 课程内容包括： 1. 核心机器学习与深度学习概念 - 了解机器学习类型、模型训练、评估和实际应用案例 - 深入研究神经网络、激活函数和深度学习工作流程 2. Python与数据科学工具 - 掌握Python在机器学习中的应用，实践使用NumPy、Pandas和Matplotlib - 夯实数据处理、可视化和预处理的基础 3. TensorFlow与TensorFlow Lite - 使用TensorFlow 2.x训练机器学习模型 - 将模型转换为TensorFlow Lite（TFLite）以便高效的本地执行 您将构建和部署的项目包括： - 线性回归及预测应用：预测燃油效率、房价等，并将这些模型直接部署到Android、iOS和Flutter应用中 - 图像分类：训练卷积神经网络（CNN）并使用Teachable Machine进行无代码模型构建，将分类模型集成到应用中以分析图像和视频流 - 目标检测与迁移学习：收集、注释并使用迁移学习训练目标检测模型，并在实时检测应用中优化和部署 移动开发与ML集成： - Flutter（Dart）：在Flutter应用中使用TFLite模型，处理图像、视频和实时摄像头流，从零开始构建跨平台ML应用 - 原生Android（Kotlin）：使用Kotlin和Android Studio集成ML模型，访问CameraX和ML Kit APIs，构建高效响应的人工智能驱动Android应用 - iOS（Swift & SwiftUI）：在Swift中加载和运行ML模型，为iPhone和iPad构建流畅轻巧的本地AI应用 课程亮点： - 实现本地ML的必要性：无需互联网 - 本地运行AI模型，确保隐私和速度 - 快速推理 - 通过TFLite进行性能优化 - 跨平台部署 - 一门课程涵盖Android、iOS和Flutter - 行业相关技能 - TensorFlow、Python、TFLite及应用集成 完成本课程后，您将构建： - 线性回归AI应用（定价、预测、效率） - 图像分类器应用（图像和视频分类） - 目标检测应用（使用移动摄像头的实时检测） 在快速变化的移动AI世界中保持领先。这是您精通本地机器学习和构建真实生产就绪移动应用的2025完整指南。立即注册并开始构建更快、更智能且完全离线的智能移动应用！
Unlock the full potential of Machine Learning (ML) and AI in mobile development with this 2025-ready, hands-on course. Whether you're a beginner, mobile developer, or a data science enthusiast, you'll learn how to build, train, and deploy real machine learning models on-device - without the need for internet or cloud services.You'll go from the foundations of ML & deep learning to creating intelligent mobile apps that run locally on Android, iOS, and Flutter, using the power of TensorFlow Lite.What You'll LearnCore ML & Deep Learning ConceptsUnderstand types of machine learning, model training, evaluation, and real-world use casesDive into neural networks, activation functions, and deep learning workflowsPython & Data Science ToolsMaster Python for ML with hands-on use of NumPy, Pandas, and MatplotlibBuild a strong foundation in data handling, visualization, and preprocessingTensorFlow & TensorFlow LiteTrain machine learning models using TensorFlow 2.xConvert models to TensorFlow Lite (TFLite) for efficient on-device executionProjects You'll Build & Deploy On-DeviceLinear Regression & Predictive AppsPredict fuel efficiency, house prices, and moreDeploy these models directly into Android, iOS, and Flutter appsImage ClassificationTrain CNNs and use Teachable Machine for no-code model buildingIntegrate classification models into apps to analyze images and video feedsObject Detection with Transfer LearningCollect, annotate, and train object detection models using transfer learningOptimize and deploy them in real-time detection appsMobile Development with ML IntegrationFlutter (Dart)Use TFLite models in Flutter appsWork with image, video, and live camera feedsBuild cross-platform ML apps from scratchNative Android (Kotlin)Integrate ML models using Kotlin and Android StudioAccess the CameraX and ML Kit APIsBuild efficient and responsive AI-powered Android appsiOS (Swift & SwiftUI)Load and run ML models in SwiftBuild smooth and lightweight on-device AI apps for iPhones and iPadsWhy On-Device ML in 2025?No Internet Needed - Run AI models locally with privacy and speedLightning Fast Inference - Optimized for performance with TFLiteCross-Platform Deployment - Android, iOS, and Flutter in one courseIndustry-Relevant Skills - TensorFlow, Python, TFLite, app integrationBy the End of This Course, You'll Have Built:Linear Regression AI Apps (pricing, prediction, efficiency)Image Classifier Apps (image & video classification)Object Detection Apps (live detection using mobile cameras)Stay ahead in the fast-changing mobile AI world. This is your 2025 complete guide to mastering on-device machine learning and building real, production-ready mobile apps with AI.Enroll now and start building intelligent mobile apps with on-device AI - faster, smarter, and completely offline.
