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所在平台: Udemy |
课程主页: https://www.udemy.com/course/artificial-intelligence-engineering/
课程评论：没有评论
课程名称：人工智能工程 概述：本课程专为希望成为机器学习与人工智能工程师的个人设计，涵盖了完整的机器学习（ML）流程，从基本原理到复杂的部署技术。参与者将通过动手项目和真实场景研究，掌握创建、优化和实施AI技术的实用技能。 该课程围绕机器学习和人工智能工程领域的角色和职责进行结构化，深入探讨所有重要方面，如机器学习算法、机器学习流程、深度学习框架、模型训练、部署以及操作的最佳实践。课程分为11个全面的部分，从基础知识开始，逐步深入到更复杂的主题。每个部分包括多个课程、实践项目和测验，以巩固所学概念。 课程的关键特点包括： - 全面覆盖：课程内容从基本的数学和Python技能到高级主题，如MLOps和大型语言模型。 - 动手项目：每个主要部分都有一个实践项目，用于应用所学概念。 - 行业相关性：课程包括关于MLOps、部署和当前AI趋势的部分，帮助学生为真实场景做准备。 - 实用技能：课程强调诸如超参数优化、模型部署和性能监控等实用技能。 - 伦理考量：包含有关人工智能伦理的讨论，这是AI工程师的重要议题。 - 顶点项目：课程最后设有为期数周的顶点项目，使学生能够以全面的方式展示他们的技能。 本课程旨在培养学员在机器学习和人工智能工程领域的专业能力，适合有志于此领域的学习者。
This in-depth course is tailored for individuals aiming to become Machine Learning and AI Engineers. It encompasses the full ML pipeline, from basic principles to sophisticated deployment techniques. Participants will engage in hands-on projects and study real-world scenarios to acquire practical skills in creating, refining, and implementing AI technologies.The Udemy course for Machine Learning and AI Engineering is structured around the roles and responsibilities within the field. It provides a thorough exploration of all essential aspects, such as ML algorithms, the ML pipeline, deep learning frameworks, model training, deployment, and best practices for operations.Organized into 11 comprehensive sections, the course begins with the basics and gradually tackles more complex subjects. Each section is comprised of several lessons, practical projects, and quizzes to solidify the concepts learned.Here are some key features of the course:Comprehensive coverage: The course covers everything from basic math and Python skills to advanced topics like MLOps and large language models.Hands-on projects: Each major section includes a practical project to apply the learned concepts.Industry relevance: The course includes sections on MLOps, deployment, and current trends in AI, preparing students for real-world scenarios.Practical skills: There's a strong focus on practical skills like hyperparameter optimization, model deployment, and performance monitoring.Ethical considerations: The course includes a discussion on AI ethics, an important topic for AI engineers.Capstone project: The course concludes with a multi-week capstone project, allowing students to demonstrate their skills in a comprehensive manner.
