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所在平台: Coursera |
课程主页: https://www.coursera.org/learn/optimize-machine-learning-model-performance
课程评论：没有评论
课程名称：优化机器学习性能 概述：本课程综合了您在应用机器学习专业化中学到的所有知识。您将通过一个完整的机器学习项目，准备一个机器学习维护路线图。您将了解和分析如何应对变化的数据，并能够识别和解释项目中潜在的意外影响。您将理解并定义操作化和维护应用机器学习模型的程序。到课程结束时，您将掌握所有必要的工具和理解，以自信地推出机器学习项目，并准备在您所在的业务上下文中进行优化。 为了取得成功，您应具备至少初级的Python编程背景（例如，能够阅读和追踪现有代码，熟悉条件语句、循环、变量、列表、字典和数组）。您还应对线性代数（向量表示法）和统计学（概率分布及均值/中位数/众数）有基本的理解。 这是Coursera和阿尔伯塔机器智能研究所（Amii）提供的应用机器学习专业化的最后一门课程。 课程大纲： 1. **机器学习战略**：本周将介绍理解业务所需的整体战略工具，以便在机器学习投资中获得最佳回报。从了解当前状况到导航所有权和组建团队，本周聚焦于在成功的商业环境中理解应用机器学习。 2. **负责任的机器学习**：本周将讨论机器学习的更广泛背景：作为开发者，我们在技术使用上有责任。通过案例研究和现有框架，我们将为您提供工具，以制定自己的伦理方法，在现实世界中实现最佳结果。 3. **机器学习在生产与规划中的应用**：机器学习在现实世界中的一个重要方面是考虑您的机器学习模型与现有系统的集成方式及其对操作的影响。本周将回顾在将量子算法和机器学习模型转化为操作工具时应该考虑的事项。 4. **机器学习系统的维护与管理**：模型上线后，工作并没有结束！在最后一周，我们将讨论在实际运行系统的背景下，您需要考虑的所有事项。
Part: 1
Title:Machine Learning Strategy
Description:This week we'll present tools for understanding the overall strategy your business needs in order to see the best returns on ML investment. From understanding the current status to navigating ownership and setting up a team, this week is about understanding applied machine learning in a successful business context.
Part: 2
Title:Responsible Machine Learning
Description:This week we'll talk about the broader context of machine learning: how as developers we have responsibilities regarding how our technology will be used. Using case studies and existing frameworks we'll give you the tools to figure out your own ethical approach to realize the best outcomes while deploying machine learning in the real world.
Part: 3
Title:Machine Learning in Production & Planning
Description:An important aspect of machine learning in the real world is considering how your machine learning models are integrated with existing systems, and what effect they have on your operations. This week we'll review things you should consider as you turn QuAMs and machine learning models into operational tools.
Part: 4
Title:Care and Feeding of your Machine Learning System
Description:Work doesn't end just because your model is deployed! In our final week we'll go over all the things you need to consider in the context of an actual working system.
This course synthesizes everything your have learned in the applied machine learning specialization. You will now walk through a complete machine learning project to prepare a machine learning maintenance roadmap. You will understand and analyze how to deal with changing data. You will also be able to identify and interpret potential unintended effects in your project. You will understand and define procedures to operationalize and maintain your applied machine learning model. By the end of this course you will have all the tools and understanding you need to confidently roll out a machine learning project and prepare to optimize it in your business context. To be successful, you should have at least beginner-level background in Python programming (e.g., be able to read and code trace existing code, be comfortable with conditionals, loops, variables, lists, dictionaries and arrays). You should have a basic understanding of linear algebra (vector notation) and statistics (probability distributions and mean/median/mode). This is the final course of the Applied Machine Learning Specialization brought to you by Coursera and the Alberta Machine Intelligence Institute (Amii).
