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所在平台: Coursera |
课程主页: https://www.coursera.org/learn/machine-learning-projects
课程评论：没有评论
课程名称：机器学习项目结构化 课程概述：在深度学习专项课程的第三门课程中，您将学习如何构建一个成功的机器学习项目，并实际练习作为机器学习项目负责人的决策制定能力。通过本课程的学习，您将能够诊断机器学习系统中的错误，优先考虑减少错误的策略，理解复杂的机器学习设置，例如训练/测试集不匹配，以及与人类水平表现的比较和超越。此外，您还将学习应用端到端学习、迁移学习和多任务学习。 此课程也是一门独立课程，适合具备基本机器学习知识的学习者。该课程借鉴了Andrew Ng在构建和交付众多深度学习产品中的经验。如果您希望成为能够为AI团队设定方向的技术领导者，这门课程将提供您在机器学习工作中几年后可能获得的“行业经验”。 深度学习专项课程是我们的基础项目，帮助您理解深度学习的能力、挑战和后果，并为您参与领先的AI技术发展做好准备。它为您提供了获得将机器学习应用于工作、提升技术职业生涯的知识和技能的途径，从而在AI领域迈出关键一步。 课程大纲： - 名称：机器学习策略 描述：通过实施战略指导来优化机器学习生产工作流程，以帮助定义关键优先事项并设置目标。 - 名称：机器学习策略 描述：开发节省时间的错误分析程序，以评估最值得追求的选项，并获取如何拆分数据的直觉，以及何时使用多任务、迁移和端到端深度学习。
Name:ML Strategy
Description:Streamline and optimize your ML production workflow by implementing strategic guidelines for goal-setting and applying human-level performance to help define key priorities.
Name:ML Strategy
Description:Develop time-saving error analysis procedures to evaluate the most worthwhile options to pursue and gain intuition for how to split your data and when to use multi-task, transfer, and end-to-end deep learning.
In the third course of the Deep Learning Specialization, you will learn how to build a successful machine learning project and get to practice decision-making as a machine learning project leader. By the end, you will be able to diagnose errors in a machine learning system; prioritize strategies for reducing errors; understand complex ML settings, such as mismatched training/test sets, and comparing to and/or surpassing human-level performance; and apply end-to-end learning, transfer learning, and multi-task learning. This is also a standalone course for learners who have basic machine learning knowledge. This course draws on Andrew Ng’s experience building and shipping many deep learning products. If you aspire to become a technical leader who can set the direction for an AI team, this course provides the "industry experience" that you might otherwise get only after years of ML work experience. The Deep Learning Specialization is our foundational program that will help you understand the capabilities, challenges, and consequences of deep learning and prepare you to participate in the development of leading-edge AI technology. It provides a pathway for you to gain the knowledge and skills to apply machine learning to your work, level up your technical career, and take the definitive step in the world of AI.
