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所在平台: Coursera |
课程主页: https://www.coursera.org/learn/foundations-of-ai-and-machine-learning
课程评论：没有评论
课程名称：人工智能与机器学习基础 概述：本课程提供了人工智能（AI）和机器学习（ML）基础设施的全面介绍。您将探索AI和ML环境的关键元素，包括数据管道、模型开发框架和部署平台。课程强调AI和ML基础设施中稳健和可扩展设计的重要性。 课程目标： 通过本课程，您将能够分析、描述和批判性讨论AI/ML基础设施的关键组成部分。 课程大纲： 1. **AI/ML环境简介** 此模块全面介绍AI和ML基础设施的基本元素，重点关注支撑有效ML和AI系统的组件和过程。您将了解支持稳健AI/ML应用所需的基础设施的关键方面，获得管理和贡献AI/ML项目的知识基础。 2. **AI/ML中的数据管理** 本模块深入探讨AI和ML背景下有效数据获取、清洗和预处理所需的复杂技术和最佳实践。着重于数据完整性和安全性，您将获得管理各种应用数据源的技能，包括在大型语言模型（LLMs）和传统ML系统中的应用。您还将学习如何确保数据在整个AI开发生命周期中的安全性。 3. **模型框架的考虑与选择** 此模块全面探讨流行的ML框架、库和预训练的LLMs。您将获得这些工具的实践经验，学习评估它们的优缺点，并根据特定项目需求选择最合适的框架。 4. **部署平台的考虑** 本模块详细探讨将ML模型部署到生产环境的关键方面。您将学习识别部署平台的关键特性，为现实世界的使用准备模型，实施版本控制以确保可重现性，并根据可扩展性和效率评估平台。 5. **AI/ML概念实践** 此模块深入探讨AI/ML工程师在企业环境中的角色演变。您将全面理解该角色的职责，包括数据管理、框架选择、部署、版本控制和云相关考虑。模块强调基础设施和运营的整合以优化结果，并提供在AI/ML社区内建立人脉和寻找导师的策略。 通过本课程结束后，您将清楚理解AI/ML工程师在企业中的演变角色、有效管理基础设施的关键操作优先事项，以及在该领域建立专业网络和寻找有价值导师的策略。
Name:Introduction to AI/ML environments
Description:This module provides a comprehensive introduction to the essential elements of AI/ML infrastructure, focusing on the components and processes that underpin effective ML and AI systems. This module will cover the critical aspects of infrastructure required to support robust AI/ML applications, from data handling to model deployment. By the end of this module, you'll have a solid foundation in AI/ML infrastructure, equipping you with the knowledge to contribute to and manage AI/ML projects effectively.
Name:Data management in AI/ML
Description:This module delves into the sophisticated techniques and best practices required for effective data acquisition, cleaning, and preprocessing in the context of AI and ML. Emphasizing the importance of data integrity and security, this module will equip you with the skills needed to manage data sources for various applications, including retrieval-augmented generation (RAG) in large language models (LLMs) and traditional ML systems. You will also learn how to ensure data security throughout the AI development life cycle. By the end of this module, you'll be proficient in advanced data acquisition, cleaning, and preprocessing techniques, and will have a solid understanding of data security best practices, enabling you to manage data effectively and securely in AI development.
Name:Considering and selecting model frameworks
Description:This module offers a comprehensive exploration of popular ML frameworks, libraries, and pretrained LLMs. You will gain hands-on experience with these tools, learning to evaluate their strengths and weaknesses and select the most suitable ones based on specific project needs. By the end of the module, you'll be equipped to implement basic models and adapt their framework choices to optimize performance for diverse applications.
Name:Considerations when deploying platforms
Description:This module provides a detailed exploration of the critical aspects of deploying ML models into production environments. You will learn to identify the key features of deployment platforms, prepare models for real-world use, implement version control for reproducibility, and evaluate platforms based on their scalability and efficiency. By the end of this module, you will be equipped to effectively deploy ML models in production environments, manage their lifecycle with version control, and select the most suitable deployment platforms based on scalability and efficiency considerations.
Name:AI/ML concepts in practice
Description:This module offers an in-depth exploration of the evolving role of AI/ML engineers within corporate environments. You will gain a comprehensive understanding of the responsibilities associated with this role, including data management, framework selection, deployment, version control, and cloud considerations. The module also emphasizes the integration of infrastructure and operations to optimize outcomes and provides strategies for networking and finding mentorship within the AI/ML community. By the end of this module, you will have a clear understanding of the AI/ML engineer's evolving role in the corporate landscape, the key operational priorities for effective infrastructure management, and strategies for building a professional network and finding valuable mentors in the field.
This course provides a comprehensive introduction to fundamental components of artificial intelligence and machine learning (AI & ML) infrastructure. You will explore the critical elements of AI & ML environments, including data pipelines, model development frameworks, and deployment platforms. The course emphasizes the importance of robust and scalable design in AI & ML infrastructure. By the end of this course, you will be able to: 1. Analyze, describe, and critically discuss the critical com
