|
所在平台: Coursera |
课程主页: https://www.coursera.org/learn/machine-learning-under-the-hood
课程评论：没有评论
课程名称：机器学习内幕：技术提示、技巧及陷阱 课程概述：机器学习是现代企业需求的重要技能，LinkedIn将其列为“企业最需要的技能”之一。这门课程适合所有希望了解机器学习运作原理的职场人士，无论您是企业领导、决策者还是运营经理，掌握基础知识对于推动项目进展至关重要。 课程内容将揭示机器学习的运作机制，涵盖数据洞察的提取、如何验证洞察的可靠性以及预测模型的性能评估。课程不仅涉及基础知识，还延伸至前沿的先进方法，并提供规避常见陷阱的策略，无论是技术背景的学习者还是非技术背景的初学者，都可以轻松理解。 在本课程中，您将学习到： - 预测建模算法的工作原理，包括决策树、逻辑回归和神经网络 - 潜在的陷阱，如过拟合、p-hacking，以及误将相关性视为因果关系 - 如何详细解读预测模型及其工作原理 - 高级方法如集成模型和提升建模（即说服建模） - 从众多机器学习软件中选择合适的工具 - 如何用商业术语评估预测模型的性能 - 如何筛选预测模型，避免对受保护群体的潜在偏见（即AI伦理） 课程由业内知名专家Eric Siegel教授主讲，内容深入却通俗易懂，适合各类学习者。尽管课程不涉及编码实践或复杂数学，但有一个评估环节会让您在Excel或Google Sheets中手动创建预测模型，充分可视化其改进效果。 适宜的学习者包括商业领袖和希望向数据科学家转型的学员。课程内容中包含众多高级技术和陷阱的阐述，帮助受众建立全面的知识体系。该课程为中立型，尽管包含SAS产品的演示，但无论您使用何种机器学习软件，学习目标均可适用。 课程大纲： 1. 模块1 - 机器学习的基础原理：探讨数据越大越危险的原因，如何避免随机噪声的干扰，并确保科学发现的可信性。 2. 模块2 - 标准机器学习方法：包括决策树、Naive Bayes、线性回归和逻辑回归，学习如何评估这些模型的性能。 3. 模块3 - 高级方法及建模软件：了解深度学习及其应用，探讨高级建模方法并比较不同模型。 4. 模块4 - 陷阱、偏见与总结：深入理解机器偏见问题及其对决策的影响，讨论模型透明度、可解释性及伦理问题。 通过这门课程，您将全面掌握机器学习的核心知识与应用，助力职业生涯的提升。
Name:MODULE 1 - The Foundational Underpinnings of Machine Learning
Description:In what way is bigger data more dangerous? How do we avoid being fooled by random noise and ensure scientific discoveries are trustworthy? This module covers the fundamental ways in which machine learning works – and doesn't work. First, we'll cover three prevalent, heartbreaking pitfalls: overfitting, p-hacking, and presuming causation when we have only ascertained correlation. Then we'll establish the foundational principles behind the design of machine learning methods.
Name:MODULE 2 - Standard, Go-To Machine Learning Methods
Description:This module covers four standard machine learning methods: decision trees, Naive Bayes, linear regression, and logistic regression. We'll show you how they work, checking their predictive performance over example datasets and visualizing their decision boundaries as a way to compare and contrast their capabilities. You'll also see how to evaluate these models in terms of lift and profit, and why improving model probability estimates is so important.
Name:MODULE 3 - Advanced Methods, Comparing Methods, & Modeling Software
Description:When should you turn to deep learning, the leading advanced machine learning method, and when is its complexity overkill? And is there a way to advance model capability and performance that's elegant and simple, without involving the complexity of neural networks? In this module, we'll cover more advanced modeling methods, including neural networks, deep learning, and ensemble models. Then we'll compare and contrast the full range of modeling methods, and we'll overview the many machine learning software tool options you have at your disposal. We'll then turn to a special, advanced method called uplift modeling (aka persuasion modeling), which goes beyond predicting an outcome to actually predicting the influence that a decision would have on that outcome. We'll explore the marketing applications of uplift modeling and see success stories from the likes of US Bank and President Obama's 2012 reelection campaign.
Name:MODULE 4 – Pitfalls, Bias, and Conclusions
Description:Crime-predicting models cannot on their own realize racial equity. It turns out that models that are racially equitable in one sense are not in another. This is often referred to as machine bias. This quandary also applies for other kinds of consequential decisions driven by predictive models, including loan approvals, insurance pricing, HR decisions, and medical triage. This module dives deep into understanding the machine bias conundrum and what recourses could be considered in response to it. We'll also ramp up on a related, emerging movement in support of model transparency, explainable machine learning, and the right to explanation. We'll then wrap up the overall three-course specialization with a summary of the ethical issues, the technical pitfalls, and your options for continuing your learning and career path in machine learning.
Machine learning. Your team needs it, your boss demands it, and your career loves it. After all, LinkedIn places it as one of the top few "Skills Companies Need Most" and as the very top emerging job in the U.S. If you want to participate in the deployment of machine learning (aka predictive analytics), you've got to learn how it works. Even if you work as a business leader rather than a hands-on practitioner – even if you won't crunch the numbers yourself – you need to grasp the underlying mechanics in order to help navigate the overall project. Whether you're an executive, decision maker, or operational manager overseeing how predictive models integrate to drive decisions, the more you know, the better. And yet, looking under the hood will delight you. The science behind machine learning intrigues and surprises, and an intuitive understanding is not hard to come by. With its impact on the world growing so quickly, it's time to demystify the predictive power of data – and how to scientifically tap it. This course will show you how machine learning works. It covers the foundational underpinnings, the way insights are gleaned from data, how we can trust these insights are reliable, and how well predictive models perform – which can be established with pretty straightforward arithmetic. These are things every business professional needs to know, in addition to the quants. And this course continues beyond machine learning standards to also cover cutting-edge, advanced methods, as well as preparing you to circumvent prevalent pitfalls that seldom receive the attention they deserve. The course dives deeply into these topics, and yet remains accessible to non-technical learners and newcomers. With this course, you'll learn what works and what doesn't – the good, the bad, and the fuzzy: – How predictive modeling algorithms work, including decision trees, logistic regression, and neural networks – Treacherous pitfalls such as overfitting, p-hacking, and presuming causation from correlations – How to interpret a predictive model in detail and explain how it works – Advanced methods such as ensembles and uplift modeling (aka persuasion modeling) – How to pick a tool, selecting from the many machine learning software options – How to evaluate a predictive model, reporting on its performance in business terms – How to screen a predictive model for potential bias against protected classes – aka AI ethics 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. NO HANDS-ON AND NO HEAVY MATH. Rather than a hands-on training, this course serves both business leaders and burgeoning data scientists alike with expansive coverage of the state-of-the-art techniques and the most pernicious pitfalls. There are no exercises involving coding or the use of machine learning software. However, for one of the assessments, you'll perform a hands-on exercise, creating a predictive model by hand in Excel or Google Sheets and visualizing how it improves before your eyes. 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 with a strong conceptual framework and covers topics that are generally omitted from even the most technical of courses, including uplift modeling (aka persuasion modeling) and some particularly treacherous pitfalls. 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. PREREQUISITES. Before this course, learners should take the first two of this specialization's three courses, "The Power of Machine Learning" and "Launching Machine Learning."
