Machine learning for managers:
Machine learning can help managers make better predictions, automate complex tasks and improve business operations. Managers who are familiar with machine learning are better placed to navigate the increasingly digital world we live in. There is a view that machine learning is a highly technical sub...
Gespeichert in:
| 1. Verfasser: | |
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| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
London ; New York
Routledge, Taylor & Francis Group
2023
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| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Zusammenfassung: | Machine learning can help managers make better predictions, automate complex tasks and improve business operations. Managers who are familiar with machine learning are better placed to navigate the increasingly digital world we live in. There is a view that machine learning is a highly technical subject that can only be understood by specialists. However, many of the ideas that underpin machine learning are straightforward and accessible to anyone with a bit of curiosity. This book is for managers who want to understand what machine learning is about, but who lack a technical background in computer science, statistics or math. The book describes in plain language what machine learning is and how it works. In addition, it explains how to manage machine learning projects within an organization. This book should appeal to anyone that wants to learn more about using machine learning to drive value in real-world organizations |
| Beschreibung: | xx, 159 Seiten Illustrationen, Diagramme 330 gr |
| ISBN: | 9781032362427 9781032362434 |
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| 520 | |a Machine learning can help managers make better predictions, automate complex tasks and improve business operations. Managers who are familiar with machine learning are better placed to navigate the increasingly digital world we live in. There is a view that machine learning is a highly technical subject that can only be understood by specialists. However, many of the ideas that underpin machine learning are straightforward and accessible to anyone with a bit of curiosity. This book is for managers who want to understand what machine learning is about, but who lack a technical background in computer science, statistics or math. The book describes in plain language what machine learning is and how it works. In addition, it explains how to manage machine learning projects within an organization. This book should appeal to anyone that wants to learn more about using machine learning to drive value in real-world organizations | ||
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Datensatz im Suchindex
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Contents xiv Overview Preface xv List of figures xvii List of tables xix Author XX I Understanding machinelearning 1 Let's jump right in 3 3 What can we learn from 34 lines of code? 3 1.2 Fitting ML into the big picture 6 1.3 A layered perspective of machine learning 7 1.1 2 1 1.4 Data, compute and methods 1.5 ML drives wealth creation 9 12 Different kinds of ML 14 2.1 An introduction to the ML zoo 14 2.2 Supervised vs unsupervised ML 14 2.3 Generative learning 16 2.4 Reinforcement learning 16 2.5 Online vs batch training 17 2.6 Value-destroying vs value-creating ML 18 Creating ML models 3.1 Data, instances and features 3.2 19 19 Targets and inputs 20 ix
Contents 4 3.3 Training, validation and test data sets 20 3.4 The machine learning recipe 24 Specify the problem 24 3.4.2 Collect the data 25 3.4.3 Split the data 25 3.4.4 Understand and explore the data 27 3.4.5 Preprocess the data and construct features 28 3.4.6 Select a machine learning approach 28 3.4.7 Select hyper-parameters 29 3.4.8 Train the model 29 3.4.9 Evaluate the model on validation data 29 3.4.10 If validation performance is weak 30 3.4.11 Train the final model 30 3.4.12 Evaluate the final model on the test data 31 3.4.13 If test performance is weak 31 3.4.14 Deploy the model in production 32 3.4.15 Monitor the model 32 Linear models 4.1 5 3.4.1 A simple linear model 34 34 4.2 Training linear regression models 35 4.3 Using feature transformations in linear models 37 4.4 Performance measures for regression tasks 37 4.5 Linear models with indicator variables and interactions 38 4.6 Classification with logistic regression 42 4.7 Regularization - ridge regression, lasso and elastic net 43 Neural networks 47 5.1 A brief history of neural networks 47 5.2 A linear model is a neural net (a very simple one) 48 5.3 All you ever wanted to know about nodes 49 5.4 More complex neural networks 50 5.5 Training a neural network 51 5.6 The MNIST example 53 5.7 A peek into the future - transformers and language models 55 x
Contents 6 Tree-based approaches, ensembles andboosting 58 6.1 The Titanic example 58 6.2 Making predictions with a tree model 61 6.3 Performance measures for classification tasks 62 6.3.1 Confusion matrices 62 6.3.2 Classification performance measures 63 6.3.3 Thresholds and the ROC-AUC measure 64 6.4 Ensembles and random forests 67 6.5 Gradient boosting machines 68 7 Dimensionality reduction and clustering 71 7.1 Why reduce dimensionality? 71 7.2 Principal components analysis 72 7.3 Clustering 75 7.3.1 K-means clustering 75 7.3.2 Hierarchical agglomerative clustering (НАС) 75 8 Unstructured data 80 8.1 Images 80 8.2 Sequences 81 8.3 Transformers 83 9 Explainable Al Why do we need explainable Al? 87 9.2 Explaining models 90 9.3 11 87 9.1 9.2.1 Intrinsically interpretable models 90 9.2.2 Surrogate models 90 Explaining predictions 91 Managing machine learning projects 97 10 The ML system lifecycle 99 10.1 Context 99 10.2 Identify 99 10.3 Pilot 100 10.4 Pipeline 101 10.5 Development 101 xi
Contents 10.6 Deployment and monitoring 102 10.7 The circle of life 102 11 The big picture 11.1 Why getting things done is hard 104 104 11.2 Governance model 106 11.3 Security and privacy 11.4 Explainability and fairness 108 110 11.5 Laws, regulations and compliance 12 Creating value with ML 112 113 12.1 Sources of value 113 12.2 The data-centric firm 115 12.3 The economics of platforms 12.4 Outside of platforms 119 122 13 Making the business case 124 13.1 Executive summary 126 13.2 Description of the project 127 13.3 Project benefits 127 13.4 Proof-of-concept 129 13.5 Required resources 13.6 Technical appendix 130 130 14 The ML pipeline 14.1 Who needs a pipeline anyway? 14.2 The ML pipeline 15 Development 15.1 A very brief introduction to software engineering 15.1.1 Divide and conquer 133 134 137 137 137 15.1.2 Expose interfaces, hide implementations 138 15.1.3 Implement incrementally 139 15.1.4 Use version control 140 15.1.5 Conduct automated testing 15.2 Validating the pipeline xii 133 140 141 15.2.1 Run-through data 15.2.2 Synthetic data 141 141 15.2.3 Trivial models 142
Contents 15.2.4 Simple benchmark models 15.2.5 Current approach 15.3 Model development 15.4 Performance vs value 15.5 Technical debt 16 Deployment and monitoring 16.1 16.2 16.3 16.4 16.5 16.6 Index Set up the production environment Connect the plumbing Test, test, test Flip the switch Continuous monitoring Final thoughts 142 142 143 144 145 147 147 148 149 149 150 152 155 xiii |
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| id | DE-604.BV049622913 |
| illustrated | Illustrated |
| index_date | 2024-07-03T23:37:23Z |
| indexdate | 2024-07-20T07:25:53Z |
| institution | BVB |
| isbn | 9781032362427 9781032362434 |
| language | English |
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| physical | xx, 159 Seiten Illustrationen, Diagramme 330 gr |
| publishDate | 2023 |
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| publisher | Routledge, Taylor & Francis Group |
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| spelling | Geertsema, Paul Verfasser aut Machine learning for managers Paul Geertsema London ; New York Routledge, Taylor & Francis Group 2023 © 2023 xx, 159 Seiten Illustrationen, Diagramme 330 gr txt rdacontent n rdamedia nc rdacarrier Machine learning can help managers make better predictions, automate complex tasks and improve business operations. Managers who are familiar with machine learning are better placed to navigate the increasingly digital world we live in. There is a view that machine learning is a highly technical subject that can only be understood by specialists. However, many of the ideas that underpin machine learning are straightforward and accessible to anyone with a bit of curiosity. This book is for managers who want to understand what machine learning is about, but who lack a technical background in computer science, statistics or math. The book describes in plain language what machine learning is and how it works. In addition, it explains how to manage machine learning projects within an organization. This book should appeal to anyone that wants to learn more about using machine learning to drive value in real-world organizations bicssc / Economics bicssc / Environmental science, engineering & technology bicssc / Automatic control engineering bicssc / Production & quality control management bicssc / E-commerce - business aspects bisacsh / BUSINESS & ECONOMICS / Industries / Computers & Information Technology bisacsh / BUSINESS & ECONOMICS / General Erscheint auch als Online-Ausgabe 978-1-003-33092-9 Digitalisierung UB Bamberg - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=034966920&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Geertsema, Paul Machine learning for managers bicssc / Economics bicssc / Environmental science, engineering & technology bicssc / Automatic control engineering bicssc / Production & quality control management bicssc / E-commerce - business aspects bisacsh / BUSINESS & ECONOMICS / Industries / Computers & Information Technology bisacsh / BUSINESS & ECONOMICS / General |
| title | Machine learning for managers |
| title_auth | Machine learning for managers |
| title_exact_search | Machine learning for managers |
| title_exact_search_txtP | Machine learning for managers |
| title_full | Machine learning for managers Paul Geertsema |
| title_fullStr | Machine learning for managers Paul Geertsema |
| title_full_unstemmed | Machine learning for managers Paul Geertsema |
| title_short | Machine learning for managers |
| title_sort | machine learning for managers |
| topic | bicssc / Economics bicssc / Environmental science, engineering & technology bicssc / Automatic control engineering bicssc / Production & quality control management bicssc / E-commerce - business aspects bisacsh / BUSINESS & ECONOMICS / Industries / Computers & Information Technology bisacsh / BUSINESS & ECONOMICS / General |
| topic_facet | bicssc / Economics bicssc / Environmental science, engineering & technology bicssc / Automatic control engineering bicssc / Production & quality control management bicssc / E-commerce - business aspects bisacsh / BUSINESS & ECONOMICS / Industries / Computers & Information Technology bisacsh / BUSINESS & ECONOMICS / General |
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