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所在平台: Coursera |
课程主页: https://www.coursera.org/learn/machine-learning-applied
课程评论：没有评论
课程名称：应用机器学习导论 概述：本课程旨在为专业人士提供机器学习的基本知识，尤其是那些希望将机器学习应用于数据分析和自动化的从业者。无论是在金融、医学、工程、商业或其他领域，此课程将介绍机器学习项目中的问题定义和数据准备。 到课程结束时，学员能够通过两种方法清晰地定义机器学习问题，识别可用的数据资源，并确定潜在的机器学习应用。此外，学员将学会如何将业务需求转化为机器学习应用，并准备数据以支持有效的机器学习项目。 该课程是Coursera与阿尔伯塔机器智能研究所（Alberta Machine Intelligence Institute）共同推出的应用机器学习专门课程的第一门课程。 课程大纲： 1. 机器学习应用导论 - 本周，您将学习什么是机器学习（ML），对比不同问题场景，并探索一些关于机器学习的常见误解。您将应用这些知识来识别构成机器学习商业解决方案的不同要素。 2. 真实世界中的机器学习 - 本周，您将学习如何将业务需求转化为机器学习问题。我们将探讨一些应用实例，以帮助您理解什么样的问题定义是有效的。明确您的问题并确保拥有必要的数据是机器学习成功的关键！ 3. 学习数据 - 本周重点在数据。您将学习数据采集，并了解各种训练数据的来源。我们还将讨论所需数据的量以及可能出现的陷阱，包括伦理问题。 4. 机器学习项目 - 本周，您将学习机器学习过程生命周期（MLPL）。理解MLPL的定义和组成部分后，您将分析MLPL在案例研究中的应用。
Name:Introduction to Machine Learning Applications
Description:This week, you will learn about what machine learning (ML) actually is, contrast different problem scenarios, and explore some common misconceptions about ML. You will apply this knowledge by identifying different components essential to a machine learning business solution.
Name:Machine Learning in the Real World
Description:This week, you will learn how to translate a business need into a machine learning problem. We'll walk through some applied examples so you can get a feel for what makes a well-defined question for your QuAM. Narrowing down your question and making sure you have the data necessary to learn is critical to ML success!
Name:Learning Data
Description:This week is all about data. You will learn about data acquisition and understand the various sources of training data. We'll talk about how much data you need and what pitfalls might arise, including ethical issues.
Name:Machine Learning Projects
Description:This week you will learn about the Machine Learning Process Lifecycle (MLPL). After understanding the definitions and components of the MLPL you will analyze the application of the MLPL on a case study.
This course is for professionals who have heard the buzz around machine learning and want to apply machine learning to data analysis and automation. Whether finance, medicine, engineering, business or other domains, this course will introduce you to problem definition and data preparation in a machine learning project. By the end of the course, you will be able to clearly define a machine learning problem using two approaches. You will learn to survey available data resources and identify potential ML applications. You will learn to take a business need and turn it into a machine learning application. You will prepare data for effective machine learning applications. This is the first course of the Applied Machine Learning Specialization brought to you by Coursera and the Alberta Machine Intelligence Institute.
