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所在平台: Udemy |
课程主页: https://www.udemy.com/course/ios-machine-learning-deployment-with-core-ml-and-vapor/
课程评论：没有评论
课程名称：iOS机器学习部署与Core ML和Vapor 课程概况：iOS机器学习部署与Core ML和Vapor是一门综合性的实践课程，旨在弥合Python基础的机器学习与Swift基础的部署之间的差距。该课程非常适合希望超越模型训练并学习如何将其集成到实际iOS应用程序中的开发人员，同时使用现代工具和最佳实践。 课程内容首先将深入Python，利用Kaggle提供的真实数据进行操作。您将学习如何清洗和预处理数据，修复格式错误的列，处理缺失值，并应用标准化和标签编码等基本数据转换技术。这些基础技能确保您的模型稳健、可靠并适合生产使用。 在数据准备充分后，您将使用scikit-learn（Python最广泛使用的机器学习库之一）训练机器学习模型。接着，您会使用Core ML工具将模型转换为Apple的Core ML格式，为顺利集成到iOS应用程序做好准备。 课程的后半部分专注于实际部署。您将把Core ML模型嵌入到基于SwiftUI的iOS应用中，学习如何设计直观的用户界面并使用训练好的模型进行实时预测。同时，您还将学习如何以用户友好的方式从模型中发送和接收数据。 为了完成全栈体验，我们引入了Vapor，Apple的开源服务器端Swift框架。您将学习如何在Vapor服务器上托管Core ML模型，并构建一个RESTful API，使iOS应用能够进行通信。这展示了如何将机器学习模型转变为实时可访问的服务，这在当今数据驱动的应用开发中是必备技能。 课程收益： - 全流程知识：从数据预处理、模型训练到移动端集成和后端部署，获得完整的开发流程体验。 - 跨学科技能：学习如何将基于Python的数据科学与基于Swift的移动和服务器开发结合起来，这在就业市场上是一项强大而稀有的技能。 - 项目成果：完成一个功能齐全的iOS应用，后端由已部署的机器学习模型支持，适合在求职面试或GitHub上展示。 - 生产级部署：理解如何构建可扩展的实时机器学习应用，通过API端点提供预测服务。 - 职业提升：无论您是希望探索移动开发的Python开发者，还是希望进入机器学习领域的iOS开发者，此课程都将极大增加您的技能和简历价值。 - 面向未来的技能：随着AI成为现代应用的核心，知道如何构建和部署机器学习驱动的功能正成为必须掌握的技能。 无论您是希望将模型带到iOS的数据科学家，还是希望扩展到机器学习的Swift开发者，本课程都将为您提供构建和部署更智能的、适合生产的应用程序所需的工具和信心。
iOS Machine Learning Deployment with Core ML and Vapor is a comprehensive, hands-on course designed to bridge the gap between Python-based machine learning and Swift-based deployment. This course is ideal for developers who want to move beyond just training models and learn how to integrate them into real-world iOS applications - all while using modern tools and best practices.We begin by diving into Python, where you'll work with real-world data sourced from Kaggle. You'll learn how to clean and preprocess this data, fix incorrectly formatted columns, handle missing values, and apply essential data transformation techniques such as standardization and label encoding. These foundational skills ensure your model is robust, reliable, and production-ready.Once your data is properly prepared, you'll train a machine learning model using scikit-learn, one of Python's most widely used ML libraries. You'll then convert the model into Apple's Core ML format using Core ML Tools, preparing it for smooth integration into iOS apps.But we don't stop there. The second half of the course focuses on real-world deployment. You'll embed your Core ML model into a SwiftUI-based iOS application, learning how to design an intuitive user interface and make real-time predictions using your trained model. You'll also learn how to send and receive data from the model in a user-friendly way.To complete the full-stack experience, we introduce Vapor, Apple's open-source server-side Swift framework. You'll learn how to host your Core ML model on a Vapor server and build a RESTful API that iOS apps can communicate with. This demonstrates how to turn your machine learning models into live, accessible services - an essential skill in today's data-driven app development landscape.How This Course Will Benefit YouEnd-to-End Knowledge: Gain the complete pipeline experience - from data preprocessing and model training to mobile integration and backend deployment.Cross-Disciplinary Skills: Learn how to combine Python-based data science with Swift-based mobile and server development - a powerful, rare skill set in the job market.Portfolio-Ready Project: Walk away with a fully functional iOS app backed by a deployed machine learning model - perfect to showcase in job interviews or on your GitHub.Production-Grade Deployment: Understand how to build scalable, real-time ML applications that can serve predictions via API endpoints.Boost Your Career: Whether you're a Python developer exploring mobile development, or an iOS developer stepping into ML, this course will add significant value to your toolkit and resume.Future-Proof Skills: With AI becoming central to modern apps, knowing how to build and deploy ML-powered features is becoming a must-have skill.Whether you're a data scientist looking to bring your models to iOS or a Swift developer aiming to expand into machine learning, this course will give you the tools and confidence to build and deploy smarter, production-ready applications.
