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所在平台: Coursera |
课程主页: https://www.coursera.org/learn/the-power-of-machine-learning
课程评论：没有评论
课程名称：《机器学习的力量：提升业务、增加点击、打击欺诈、拒绝失信者》 课程概述：在机器学习的时代，企业正在充分利用这一技术的强大能力来应对风险、提升销售、降低成本、阻止欺诈、简化制造、打击垃圾邮件、加强打击犯罪以及赢得选举。如果你想利用这一潜力，开始一个全面且面向业务的机器学习课程是最好的选择，无论你是技术人员还是商业人士。成功实施机器学习不仅依赖于技术技能，也需要精明的商业领导力。因此，不仅数据科学家需要学习基本知识，企业高管、决策者和业务经理也必须了解机器学习的运作方式及其如何创造商业价值。 本课程将为参与机器学习的部署做好准备，无论你是企业领导者还是量化分析师。课程内容超越了典型的机器学习课程，它不仅涵盖技术基础和核心定量技术，还独特地结合了实施机器学习所需的商业和技术知识。课程主要涵盖以下内容： - 启动机器学习（即预测分析）如何改善营销、金融服务、欺诈检测等多个业务操作 - 一种具体且易于理解的预测建模方法指南，深入探讨决策树 - 机器学习的预测性能报告及其带来的利润 - 在应用机器学习之前，数据需要具备的特性 - 避免“人工智能”的炒作和虚假承诺 - AI伦理：社会公正问题，例如当预测模型明显存在对受保护群体的歧视时 本课程不涉及实际操作和复杂数学，完全适合商业领袖，也是希望确保业务相关性的数据科学家的必修课。无论你将在业务侧还是技术侧参与机器学习的商业部署，该课程都适合你。这包括各类商业专业人士和决策者，如高管、董事、业务经理和顾问，以及数据科学家。 技术学习者应重新审视。在直接进行动手操作之前，应考虑一下：该课程提供的知识是所有优秀技术人员必须掌握的补充内容，帮助你理解成功部署预测模型所需的完整流程，从而产生商业影响。 类似于大学课程。本课程也非常适合大学生，特别是那些计划参加或已经在MBA项目中学习的人。整个三门课程的广度和深度相当于一学期的MBA或研究生课程。 深入且易于理解。该课程由行业领袖Eric Siegel提供，他曾在哥伦比亚大学担任教授并获奖。课程内容被认为是关于机器学习领域最全面、最吸引人且相对易懂的课程之一。 中立于供应商。虽然本课程包括机器学习在行动中的SAS产品展示，但课程内容是中立的，适用于任何机器学习软件工具。 课程大纲： - 模块0：介绍 - 模块1：机器学习的影响 - 模块2：数据：新石油 - 模块3：预测模型：从数据中学到的内容 - 模块4：行业视角：AI神话与真正的伦理风险 本课程为学习和应用机器学习的实践者提供了全面且易于理解的路径，促使 business 与 tech 两方有效合作，共同应对现代商业挑战。
Name:MODULE 0 - Introduction
Description:What does this course – and the overall three-course specialization – cover and why is it right for you? Find out how this unique curriculum will empower you to generate value with machine learning. This module outlines the specialization's unusually holistic coverage and its applicability for both business-level and tech-focused learners. You'll see why this integrated coverage is a valuable place to begin, as you prepare to take on the end-to-end process of deploying machine learning. This module will orient you and frame the upcoming content – as such, it has no assessments.
Name:MODULE 1 - The Impact of Machine Learning
Description:This module covers the business value of machine learning, the very purpose that it serves. You'll see what kinds of business operations machine learning improves and how it improves them. And we'll lay the foundation: what the data needs to look like, what is learned from that data, and how the predictions generated by machine learning render all kinds of large-scale operations more effective.
Name:MODULE 2 - Data: the New Oil
Description:We are up to our ears in data, but how much can this raw material really tell us? And what actually makes it predictive? This module will show you what your data needs to look like before your computer can learn from it – the particular form and format – and you'll see the kinds of fascinating and bizarre predictive insights discovered within that data. Then we'll take the first steps in forming a predictive model, a mechanism that serves to combine such insights.
Name:MODULE 3 - Predictive Models: What Gets Learned from Data
Description:And now the main event: predictive modeling. This module will show you how software automatically generates a predictive model from data and the elegant trick that's universally applied in order to verify that the model actually works. We'll visually compare and contrast popular modeling methods and demonstrate how to draw a profit curve that estimates the bottom line that will be delivered by deploying a model. Then we'll take a hard look at both the potential and limits of machine learning – how far advanced methods like deep learning could propel us, and yet why fundamental data requirements ultimately impose certain restrictions.
Name:MODULE 4 - Industry Perspective: AI Myths and Real Ethical Risks
Description:Machine learning is sometimes referred to as "artificial intelligence", but that ill-defined term overpromises and confuses just as much as it elicits excitement. The first portion of this module will clear up common myths about AI and show you its downside, the costs incurred by legitimizing AI as a field. Then we'll turn to the great ethical responsibilities you are taking on by entering the field of machine learning. You'll see five ways that machine learning threatens social justice and we'll dive more deeply into one: discriminatory models that base their decisions in part on a protected class like race, religion, or sexual orientation. But then we'll shift gears and balance this out by defending machine learning, demonstrating all the good it does in the world and holding its criticisms up to a higher standard.
It's the age of machine learning. Companies are seizing upon the power of this technology to combat risk, boost sales, cut costs, block fraud, streamline manufacturing, conquer spam, toughen crime fighting, and win elections. Want to tap that potential? It's best to start with a holistic, business-oriented course on machine learning – no matter whether you’re more on the tech or the business side. After all, successfully deploying machine learning relies on savvy business leadership just as much as it relies on technical skill. And for that reason, data scientists aren't the only ones who need to learn the fundamentals. Executives, decision makers, and line of business managers must also ramp up on how machine learning works and how it delivers business value. And the reverse is true as well: Techies need to look beyond the number crunching itself and become deeply familiar with the business demands of machine learning. This way, both sides speak the same language and can collaborate effectively. This course will prepare you to participate in the deployment of machine learning – whether you'll do so in the role of enterprise leader or quant. In order to serve both types, this course goes further than typical machine learning courses, which cover only the technical foundations and core quantitative techniques. This curriculum uniquely integrates both sides – both the business and tech know-how – that are essential for deploying machine learning. It covers: – How launching machine learning – aka predictive analytics – improves marketing, financial services, fraud detection, and many other business operations – A concrete yet accessible guide to predictive modeling methods, delving most deeply into decision trees – Reporting on the predictive performance of machine learning and the profit it generates – What your data needs to look like before applying machine learning – Avoiding the hype and false promises of “artificial intelligence” – AI ethics: social justice concerns, such as when predictive models blatantly discriminate by protected class NO HANDS-ON AND NO HEAVY MATH. This concentrated entry-level program is totally accessible to business leaders – and yet totally vital to data scientists who want to secure their business relevance. It's for anyone who wishes to participate in the commercial deployment of machine learning, no matter whether you'll play a role on the business side or the technical side. This includes business professionals and decision makers of all kinds, such as executives, directors, line of business managers, and consultants – as well as data scientists. BUT TECHNICAL LEARNERS SHOULD TAKE ANOTHER LOOK. Before jumping straight into the hands-on, as quants are inclined to do, consider one thing: This curriculum provides complementary know-how that all great techies also need to master. It contextualizes the core technology, guiding you on the end-to-end process required to successfully deploy a predictive model so that it delivers a business impact. LIKE A UNIVERSITY COURSE. This course is also a good fit for college students, or for those planning for or currently enrolled in an MBA program. The breadth and depth of the overall three-course specialization is equivalent to one full-semester MBA or graduate-level course. IN-DEPTH YET ACCESSIBLE. Brought to you by industry leader Eric Siegel – a winner of teaching awards when he was a professor at Columbia University – this curriculum stands out as one of the most thorough, engaging, and surprisingly accessible on the subject of machine learning. VENDOR-NEUTRAL. This course includes illuminating software demos of machine learning in action using SAS products. However, the curriculum is vendor-neutral and universally-applicable. The contents and learning objectives apply, regardless of which machine learning software tools you end up choosing to work with.
