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所在平台: Coursera |
课程主页: https://www.coursera.org/learn/launching-machine-learning-leadership
课程评论：没有评论
课程名称：启动机器学习：以金标准机器学习领导力实现运营成功 概述：机器学习正在改变世界。它为每一个客户、员工、选民和嫌疑人生成预测，这些预测使百万个商业决策更加高效。然而，要实现这一目标，企业领导与技术知识之间的鸿沟必须得到弥合。启动机器学习不仅仅是技术挑战，更是管理挑战。它的成功依赖于特定的商业领导实践，要求业务领导者与量化分析师的协调配合。 本课程将指导您如何领导或参与机器学习（即预测分析）的端到端实施。与大多数机器学习课程不同，本课程帮助您避免在建立和计划运营部署路径之前急于进行数字计算这一常见管理失误。 无论您是在机器学习项目的商业部门还是技术部门参与，本课程都将提供必需的知识。您将学习确保核心技术在商业运营中成功运作并产生价值所需的商业基础知识。如果您更倾向于量化分析，那么这是一个难得的机会让您了解商业方面，因为技术性机器学习培训通常不涉及这一领域。但请注意：软技能往往是最难掌握的。 完成本课程后，您将能够： - 应用机器学习：识别机器学习可以改善的机会领域，如市场营销、销售、金融信用评分、保险、欺诈检测等。 - 规划机器学习：确定机器学习的运营整合和部署方式，以及所需的人力和数据资源。 - 绿灯机器学习：预测机器学习项目的有效性并进行内部推广，争取同事的支持。 - 领导机器学习：管理机器学习项目，从生成预测模型到其发布。 - 为机器学习准备数据：监督数据准备工作，直接关注商业优先事项。 - 评估机器学习：以商业术语报告预测模型的表现，例如利润和投资回报率。 - 监管机器学习：管理伦理风险，例如预测模型揭示的关于个人的敏感信息，包括怀孕、辞职或可能被逮捕等情况。 本课程无需动手实践或复杂数学。它为商业领袖和新晋数据科学家提供了核心技术的背景知识，指导您了解成功部署预测模型所需的完整流程，从而实现商业影响。课程中不涉及编码或机器学习软件的操作练习。 适合人群：本课程针对有意参与机器学习商业部署的任何人，无论是在企业领导者还是量化分析师的角色。包括各类商务专业人士和决策者，如高管、经理及顾问，以及数据科学家。 课程结构类似于大学课程，非常适合大学生或在读MBA学生。整个三门课程的广度和深度相当于一个完整学期的MBA或研究生水平课程。 课程内容深入且易于理解，由行业领袖Eric Siegel教授授课，他在哥伦比亚大学任教期间荣获多项教学奖。该课程的课程设置被视为机器学习领域中最全面、最引人入胜且易于接触。 尽管课程中包含使用SAS产品的机器学习演示，但课程内容是中立的，通用适用，不论您选择使用何种机器学习软件工具。 先决条件：在参与本课程之前，学习者需完成本系列三门课程中的第一门《机器学习的力量：提升商业、积累点击、打击欺诈和拒绝失信人》。
Name:MODULE 1 - Business Applications of Machine Learning
Description:This module dives deeply into the business applications of machine learning – for marketing, financial services, fraud detection and more. We'll illustrate the value delivered for these domains by way of case studies and detailed examples. And we'll precisely measure the performance of the predictive models themselves, focusing on model lift, a predictive multiplier that tells you the improvement achieved by a model.
Name:MODULE 2 - Scoping, Greenlighting, and Managing Machine Learning Initiatives
Description:To make machine learning work, you've got to bridge what is a prevalent gap between business leadership and technical know-how. Launching machine learning is as much a management endeavor as a technical one. Its success relies on a very particular business leadership practice. This module will demonstrate that practice, guiding you to lead the end-to-end implementation of machine learning.
Name:MODULE 3 - Data Prep: Preparing the Training Data
Description:The greatest technical hands-on bottleneck of a machine learning project is the preparation of the training data – which is the raw material that predictive modeling software crunches, munches, and learns from. This module will guide you to prepare that data. Business priorities are front and center in the process, since they directly inform the data requirements, including the specific meaning of the dependent variable, which is the outcome or behavior your model will actually predict.
Name:MODULE 4 - The High Cost of False Promises, False Positives, and Misapplied Models
Description:For many machine learning projects, high accuracy is unattainable – and, besides, accuracy isn't the right metric in the first place. The first portion of this module will demonstrate how other metrics, such as the costs incurred by prediction errors, better serve to keep a machine learning project on track. Then we'll turn to the social good that can be achieved with machine learning, and we'll cover more social justice risks, including the hazards of predicting sensitive information such as pregnancy, job resignations, death, and ethnicity. We'll wrap up by examining the promise and perils of predictive policing.
Machine learning runs the world. It generates predictions for each individual customer, employee, voter, and suspect, and these predictions drive millions of business decisions more effectively, determining whom to call, mail, approve, test, diagnose, warn, investigate, incarcerate, set up on a date, or medicate. But, to make this work, you've got to bridge what is a prevalent gap between business leadership and technical know-how. Launching machine learning is as much a management endeavor as a technical one. Its success relies on a very particular business leadership practice. This means that two different species must cooperate in harmony: the business leader and the quant. This course will guide you to lead or participate in the end-to-end implementation of machine learning (aka predictive analytics). Unlike most machine learning courses, it prepares you to avoid the most common management mistake that derails machine learning projects: jumping straight into the number crunching before establishing and planning for a path to operational deployment. Whether you'll participate on the business or tech side of a machine learning project, this course delivers essential, pertinent know-how. You'll learn the business-level fundamentals needed to ensure the core technology works within - and successfully produces value for - business operations. If you're more a quant than a business leader, you'll find this is a rare opportunity to ramp up on the business side, since technical ML trainings don't usually go there. But know this: The soft skills are often the hard ones. After this course, you will be able to: - Apply ML: Identify the opportunities where machine learning can improve marketing, sales, financial credit scoring, insurance, fraud detection, and much more. - Plan ML: Determine the way in which machine learning will be operationally integrated and deployed, and the staffing and data requirements to get there. - Greenlight ML: Forecast the effectiveness of a machine learning project and then internally sell it, gaining buy-in from your colleagues. - Lead ML: Manage a machine learning project, from the generation of predictive models to their launch. - Prep data for ML: Oversee the data preparation, which is directly informed by business priorities. - Evaluate ML: Report on the performance of predictive models in business terms, such as profit and ROI. - Regulate ML: Manage ethical pitfalls, such as when predictive models reveal sensitive information about individuals, including whether they're pregnant, will quit their job, or may be arrested - aka AI ethics. NO HANDS-ON AND NO HEAVY MATH. Rather than a hands-on training, this course serves both business leaders and burgeoning data scientists alike by contextualizing 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. There are no exercises involving coding or the use of machine learning software. WHO IT'S FOR. This concentrated entry-level program is for anyone who wishes to participate in the commercial deployment of machine learning, no matter whether you'll do so in the role of enterprise leader or quant. 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. 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 specialization 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. PREREQUISITES. Before this course, learners should take the first of this specialization's three courses, "The Power of Machine Learning: Boost Business, Accumulate Clicks, Fight Fraud, and Deny Deadbeats."
