標(biāo)題: Titlebook: Introducing HR Analytics with Machine Learning; Empowering Practitio Christopher M. Rosett,Austin Hagerty Book 2021 Springer Nature Switzer [打印本頁] 作者: Garfield 時(shí)間: 2025-3-21 16:14
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作者: 我不重要 時(shí)間: 2025-3-21 23:58 作者: 消息靈通 時(shí)間: 2025-3-22 00:38 作者: 阻撓 時(shí)間: 2025-3-22 07:22
https://doi.org/10.1007/978-3-030-67626-1machine learning; big data; people analytics; human resources; workforce analytics; statistical learning; 作者: 稱贊 時(shí)間: 2025-3-22 10:28 作者: 欄桿 時(shí)間: 2025-3-22 13:22 作者: lanugo 時(shí)間: 2025-3-22 17:15 作者: 破譯 時(shí)間: 2025-3-22 23:12 作者: 文藝 時(shí)間: 2025-3-23 04:20 作者: 微生物 時(shí)間: 2025-3-23 08:20
esteht. Zu ihrer Beantwortung sei auf Abb. 197 verwiesen, die einen Teil einer Ei nph ?sen maschin e darstellt. Die Spule 1—2 des St?nders werde in einem bestimmten Augenblicke in der durch Kreuz und Punkt gekennzeichneten Richtung von dem der Wicklung zugeführten Wechselstrom durchfl?ssen. Die durc作者: DEAF 時(shí)間: 2025-3-23 12:27 作者: 盲信者 時(shí)間: 2025-3-23 16:41
Christopher M. Rosett,Austin Hagertyesteht. Zu ihrer Beantwortung sei auf Fig. 168 verwiesen, die einen Teil einer Einphasenmaschine darstellt. Die Spule 1–2 des Ankers werde in einem bestimmten Augenblicke in der durch Kreuz und Punkt gekennzeichneten Richtung von dem der Wicklung zugeführten Wechselstrom durchflossen. Die durch Glei作者: MULTI 時(shí)間: 2025-3-23 21:29 作者: 即席演說 時(shí)間: 2025-3-23 23:41
Christopher M. Rosett,Austin Hagertyesteht. Zu ihrer Beantwortung sei auf Abb. 199 verwiesen, die einen Teil einer Einphasenmaschine darstellt. Die Spule . des St?nders werde in einem bestimmten Augenblicke in der durch Kreuz und Punkt gekennzeichneten Richtung von dem der Wicklung zugeführten Wechselstrom durchflossen. Die durch Glei作者: 演講 時(shí)間: 2025-3-24 03:50
Christopher M. Rosett,Austin Hagertyesteht. Zu ihrer Beantwortung sei auf Abb. 199 verwiesen, die einen Teil einer Einphasenmaschine darstellt. Die Spule . des St?nders werde in einem bestimmten Augenblicke in der durch Kreuz und Punkt gekennzeichneten Richtung von dem der Wicklung zugeführten Wechselstrom durchflossen. Die durch Glei作者: rectocele 時(shí)間: 2025-3-24 08:30
Christopher M. Rosett,Austin Hagertyate, die sich einer immer steigenden Beliebtheit erfreuen. Sie besitzen als wesentlichen Bestandteil einen Heizk?rper aus Widerstandsdraht, der in den elektrischen Stromkreis eingeschaltet wird. Durch hohen Wirkungsgrad zeichnen sich die direkt beheizten Kochgef??e aus. Bei diesen werden durch sorgf作者: 欺騙世家 時(shí)間: 2025-3-24 12:40
ochapparate, die sich einer immer steigenden Beliebtheit erfreuen. Sie besitzen als wesentlichen Bestandteil einen Heizk?rper, der in den elektrischen Stromkreis eingeschaltet wird und meistens aus Draht von hohem Widerstand oder auch, wie bei den Apparaten der Gesellschaft ?Prometheus“, aus einer ?作者: 放逐 時(shí)間: 2025-3-24 18:14 作者: liposuction 時(shí)間: 2025-3-24 19:49 作者: plasma 時(shí)間: 2025-3-25 02:37
Christopher M. Rosett,Austin Hagerty Verh?ltnisse sind nicht un?hnlich denjenigen in einem Gas- oder einem Wasserwerke. In Gasanstalten wird zur Aufspeicherung des Gases ein Gasometer vorgesehen. In Wasserwerken wird ein Hochbeh?lter angewendet, der mit dem Rohrleitungsnetz und den Pumpen in Verbindung steht. Die Einrichtung eines sol作者: Fallibility 時(shí)間: 2025-3-25 04:40 作者: 懲罰 時(shí)間: 2025-3-25 11:34 作者: Terminal 時(shí)間: 2025-3-25 11:45 作者: 爭(zhēng)論 時(shí)間: 2025-3-25 16:50
Introductionion is increasingly steeped in the fields of mathematics and computer science. Second, employee data is different than other kinds of data and so the growth of HR Analytics is more complex than a lift-and-shift approach from other industries. And third, professionals dealing with this work usually c作者: 初次登臺(tái) 時(shí)間: 2025-3-25 20:42
Analytics About Employees and (3) HR Digital Transformation. It then differentiates where analytics falls within these categories by introducing three main types of analytics: (1) Descriptive, (2) Predictive, and (3) Prescriptive and explains the high-level definitions along with general applications of each. Finally, chapt作者: 斥責(zé) 時(shí)間: 2025-3-26 02:54
HR Analytics Ikigai or team must have balance across four critical domains: Computing, Statistics and Research Methods, Human Behavior, and Business Acumen. Each area brings its own critical value to the HR Analytics equation and must be considered if an HR Analytics team is to function. Thi chapter then puts machine 作者: LANCE 時(shí)間: 2025-3-26 06:16 作者: harpsichord 時(shí)間: 2025-3-26 10:49 作者: 饑荒 時(shí)間: 2025-3-26 16:24 作者: Ccu106 時(shí)間: 2025-3-26 18:52 作者: APO 時(shí)間: 2025-3-26 23:21 作者: cliche 時(shí)間: 2025-3-27 02:34
Common Machine Learning Techniquesbuild and tune models, but rather an introduction to how these techniques work, curated for the novice data scientist or HR practitioner. Topics include linear and polynomial regression, logistic regression, K-nearest neighbors, SVM’s, decision trees, random forests (for classification and regressio作者: 笨拙的我 時(shí)間: 2025-3-27 06:46 作者: Evacuate 時(shí)間: 2025-3-27 13:18
Machine Learning Project Managementchapter introduces project management from the machine learning perspective. For the current or future HR practitioner, this chapter explains why project management is important for machine learning projects and introduces how machine learning projects are different from other types of HR projects. 作者: moratorium 時(shí)間: 2025-3-27 15:59
The 3 A’s of a Machine Learning Projectparing traditional project management frameworks from Chap. . (Waterfall and Agile) to a common machine learning-specific methodology in use today (CRISP-DM), this chapter introduces a unique take on project management, called “The Three A’s of a Machine Learning Project.” All machine learning proje作者: murmur 時(shí)間: 2025-3-27 18:51 作者: audiologist 時(shí)間: 2025-3-28 01:25
Bringing Your Model to Lifel development—illustrating the difference between finding and preparing data versus using it to build and tune a model. This chapter dives into the iterative nature of this process and shows the feedback loops that exist between the Appreciate and Assemble phases, as well as the feedback loops that 作者: 四目在模仿 時(shí)間: 2025-3-28 04:00 作者: 愛社交 時(shí)間: 2025-3-28 06:47 作者: PARA 時(shí)間: 2025-3-28 11:58 作者: Sputum 時(shí)間: 2025-3-28 14:40 作者: clarify 時(shí)間: 2025-3-28 20:57
Book 2021itioners can harness new information to help guide positive change in the workplace. In order for today’s organizational psychologists to successfully work with their partners they must go beyond behavioral science into the realms of computing and business acumen. Similarly, today’s data scientists 作者: entail 時(shí)間: 2025-3-29 02:24
Introductiond to get oriented to the realities of HR. This chapter concludes with a commitment to deliver content for both types of professionals in order to facilitate growth and provide value for all who are interested in getting started with HR Analytics and its more advanced applications.作者: 把手 時(shí)間: 2025-3-29 07:02 作者: Judicious 時(shí)間: 2025-3-29 07:13 作者: 厚顏 時(shí)間: 2025-3-29 13:31 作者: –scent 時(shí)間: 2025-3-29 16:20
Machine Learning Project Managements, higher-than-normal ambiguity in ROI and business cases, and higher-than-normal post-implementation resource requirements. These and more are introduced to compare machine learning with more typical forms of HR project management.作者: menopause 時(shí)間: 2025-3-29 20:03 作者: 癡呆 時(shí)間: 2025-3-30 03:28
HR Analytics Ikigailearning into the mix and discusses how this implicit lean toward computer science and statistics may accidentally unbalance Ikigai. It is up to the practitioner to ensure balance is maintained so HR Analytics can be done well.作者: 延期 時(shí)間: 2025-3-30 05:06
Thinking About Your Problem-Solving Strategiesachine learning. Finally, this chapter considers that while machine learning stands to offer a lot to the world of applied analytics, it can also implicitly unbalance Analytics Ikigai so the prudent practitioner must take care to ensure this balance is maintained.作者: generic 時(shí)間: 2025-3-30 11:55
Introducing Machine Learningised versus unsupervised methods, classification versus regression, transparency, overfitting, and others are defined and introduced in preparation for a review of specific techniques in the next chapter.作者: CRANK 時(shí)間: 2025-3-30 12:31
The 3 A’s of a Machine Learning Projectcts can be broken down into Appreciate, Assemble, and Adopt phases where the project team learns the business and data context of the proposed project, develops a model which adds value to the business, and then implements and maintains a solution. This chapter introduces the overall philosophy and reviews the Appreciate phase.作者: 保全 時(shí)間: 2025-3-30 20:00
dvantage through data.Explains how machine learning actually.This book directly addresses the explosion of literature about leveraging analytics with employee data and how organizational psychologists and practitioners can harness new information to help guide positive change in the workplace. In or作者: 一個(gè)姐姐 時(shí)間: 2025-3-30 23:47
siderations are magnified by the introduction and acceleration of machine learning in HR.. .This book will serve as an introduction to these areas and provide guidance on building the connectivity across domain978-3-030-67628-5978-3-030-67626-1作者: 使人入神 時(shí)間: 2025-3-31 03:16
Book 2021s necessary to have high performing HR Analytics functions. And importantly, all these considerations are magnified by the introduction and acceleration of machine learning in HR.. .This book will serve as an introduction to these areas and provide guidance on building the connectivity across domain
