Kooperativer Bibliotheksverbund

UID:

Format: 1 Online-Ressource (XI, 96 p. 1 illus).

Edition: 1st ed. 2023

ISBN: 978-981-1988-14-1

Series Statement: SpringerBriefs in Computer Science

Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 978-981-1988-13-4

Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 978-981-1988-15-8

Language: English

DOI: 10.1007/978-981-19-8814-1

URL: Volltext  (URL des Erstveröffentlichers)

UID:

Format: 1 Online-Ressource (XIV, 336 Seiten).

ISBN: 978-981-1686-15-3

Series Statement: Machine Learning: Foundations, Methodologies, and Applications

Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 978-981-1686-14-6

Language: English

Subjects: Computer Science

Keywords: Künstliche Intelligenz ; Python 3.0

DOI: 10.1007/978-981-16-8615-3

URL: Volltext  (URL des Erstveröffentlichers)

UID:

Format: XIII, 383 p. 730 illus., 550 illus. in color. , online resource.

Edition: 1st ed. 2023.

ISBN: 9789819945580

Series Statement: Machine Learning: Foundations, Methodologies, and Applications,

Content: Artificial intelligence (AI) is rapidly gaining significance in the business world. With more and more organizations adopt AI technologies, there is a growing demand for business leaders, managers, and practitioners who can harness AI's potential to improve operations, increase efficiency, and drive innovation. This book aims to help management professionals exploit the predictive powers of AI and demonstrate to AI practitioners how to apply their expertise in fundamental business operations. It showcases how AI technology innovations can enhance various aspects of business management, such as business strategy, finance, and marketing. Readers interested in AI for business management will find several topics of particular interest, including how AI can improve decision-making in business strategy, streamline operational processes, and enhance customer satisfaction. As AI becomes an increasingly important tool in the business world, this book offers valuable insights into how it can be applied to various industries and business settings. Through this book, readers will gain a better understanding of how AI can be applied to improve business management practices and practical guidance on how to implement AI projects in a business context. This book also provides practical guides on how to implement AI projects in a business context using Python programming. By reading this book, readers will be better equipped to make informed decisions about how to leverage AI for business success.

Note: Part I: Artificial Intelligence Algorithms -- Chapter 1. Introduction to Artificial Intelligence -- Chapter 2. Regression -- Chapter 3. Classification -- Chapter 4. Clustering -- Chapter 5. Time Series -- Chapter 6. Convolutional Neural Networks -- Chapter 7. Text Mining -- Chapter 8. Chatbot, Speech and NLP -- Part II: Applications of Artificial Intelligence in Business Management -- Chapter 9. AI in Human Resource Management -- Chapter 10. AI in Sales -- Chapter 11. AI in Marketing -- Chapter 12. AI in Supply Chain Management -- Chapter 13. AI in Operations Management -- Chapter 14. AI in Corporate Finance -- Chapter 15. AI in Business Law -- Chapter 16. AI in Business Strategy -- References -- Index.

In: Springer Nature eBook

Additional Edition: Printed edition: ISBN 9789819945573

Additional Edition: Printed edition: ISBN 9789819945597

Additional Edition: Printed edition: ISBN 9789819945603

Language: English

Subjects: Computer Science

DOI: 10.1007/978-981-99-4558-0

URL: https://doi.org/10.1007/978-981-99-4558-0

URL: Volltext  (URL des Erstveröffentlichers)

UID:

Format: 1 Online-Ressource (XI, 96 p. 1 illus).

Edition: 1st ed. 2023

ISBN: 978-981-1988-14-1

Series Statement: SpringerBriefs in Computer Science

Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 978-981-1988-13-4

Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 978-981-1988-15-8

Language: English

DOI: 10.1007/978-981-19-8814-1

URL: Volltext  (URL des Erstveröffentlichers)

UID:

Format: 1 Online-Ressource (XIV, 336 Seiten).

ISBN: 978-981-1686-15-3

Series Statement: Machine Learning: Foundations, Methodologies, and Applications

Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 978-981-1686-14-6

Language: English

Subjects: Computer Science

Keywords: Künstliche Intelligenz ; Python 3.0

DOI: 10.1007/978-981-16-8615-3

URL: Volltext  (URL des Erstveröffentlichers)

UID:

Format: 1 Online-Ressource (XIV, 336 Seiten).

ISBN: 978-981-1686-15-3

Series Statement: Machine Learning: Foundations, Methodologies, and Applications

Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 978-981-1686-14-6

Language: English

Subjects: Computer Science

Keywords: Künstliche Intelligenz ; Python 3.0

DOI: 10.1007/978-981-16-8615-3

URL: Volltext  (URL des Erstveröffentlichers)

UID:

Format: 1 Online-Ressource (XI, 96 p. 1 illus).

Edition: 1st ed. 2023

ISBN: 978-981-1988-14-1

Series Statement: SpringerBriefs in Computer Science

Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 978-981-1988-13-4

Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 978-981-1988-15-8

Language: English

DOI: 10.1007/978-981-19-8814-1

URL: Volltext  (URL des Erstveröffentlichers)

UID:

Format: 1 Online-Ressource (XIV, 336 Seiten)

ISBN: 9789811686153

Series Statement: Machine Learning: Foundations, Methodologies, and Applications

Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 978-981-1686-14-6

Language: English

Subjects: Computer Science

Keywords: Künstliche Intelligenz ; Python 3.0

DOI: 10.1007/978-981-16-8615-3

URL: Volltext  (URL des Erstveröffentlichers)

UID:

Format: 1 Online-Ressource (XI, 96 p. 1 illus)

Edition: 1st ed. 2023

ISBN: 9789811988141

Series Statement: SpringerBriefs in Computer Science

Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 978-981-1988-13-4

Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 978-981-1988-15-8

Language: English

DOI: 10.1007/978-981-19-8814-1

URL: Volltext  (URL des Erstveröffentlichers)

UID:

Format: 1 online resource (385 pages)

Edition: First edition.

ISBN: 981-9945-58-5

Series Statement: Machine Learning

Note: Intro -- Preface -- Acknowledgments -- Contents -- Part I Artificial Intelligence Algorithms -- 1 Introduction to Artificial Intelligence -- 1.1 Introduction -- 1.2 History of Artificial Intelligence -- 1.3 Types of Artificial Intelligence Algorithms -- 1.4 Organization of the Book -- References -- 2 Regression -- 2.1 Linear Regression -- 2.2 Decision Tree Regression -- 2.3 Random Forests -- 2.4 Neural Network -- 2.5 Improving Regression Performance -- 2.5.1 Boxplot -- 2.5.2 Remove Outlier -- 2.5.3 Remove NA -- 2.5.4 Feature Importance -- Exercises -- References -- 3 Classification -- 3.1 Logistic Regression -- 3.2 Decision Tree and Random Forest -- 3.3 Neural Network -- 3.4 Support Vector Machines -- 3.4.1 Important Hyperparameters -- 3.5 Naive Bayes -- 3.6 Improving Classification Performance -- Exercises -- References -- 4 Clustering -- 4.1 Introduction to Clustering -- 4.2 K-means -- 4.3 The Elbow Method -- Exercises -- References -- 5 Time Series -- 5.1 Introduction to Time Series -- 5.2 Stationarity -- 5.3 Level, Trend, and Seasonality -- 5.4 Exponential Smoothing -- 5.4.1 Simple Exponential Smoothing -- 5.4.2 Double Exponential Smoothing (Holt's Exponential Smoothing) -- 5.4.3 Triple Exponential Smoothing (Holt-Winters Exponential Smoothing) -- 5.5 Moving Average Smoothing -- 5.6 Autoregression -- 5.7 Moving Average Process -- 5.8 SARIMA -- 5.9 ARCH/GARCH -- Exercises -- References -- 6 Convolutional Neural Networks -- 6.1 The Convolution Operation -- 6.2 Pooling -- 6.3 Flattening -- 6.4 Building a CNN -- 6.5 CNN Architectures -- 6.5.1 VGG16 -- 6.5.2 InceptionNet -- 6.5.3 ResNet -- 6.6 Finetuning -- 6.7 Other Tasks That Use CNNs -- 6.7.1 Object Detection -- 6.7.2 Semantic Segmentation -- Exercises -- References -- 7 Text Mining -- 7.1 Preparing the Data -- 7.2 Texts for Classification -- 7.3 Vectorize -- 7.4 TF-IDF -- 7.5 Web Scraping. , 7.6 Tokenization -- 7.7 Part of Speech Tagging -- 7.8 Stemming and Lemmatization -- Exercises -- Reference -- 8 Chatbot, Speech, and NLP -- 8.1 Speech to Text -- 8.2 Preparing the Data for Chatbot -- 8.2.1 Download the Data -- 8.2.2 Reading the Data from the Files -- 8.2.3 Preparing Data for Seq2Seq Model -- 8.3 Defining the Encoder-Decoder Model -- 8.4 Training the Model -- 8.5 Defining Inference Models -- 8.6 Talking with Our Chatbot -- Exercises -- References -- Part II Applications of Artificial Intelligence in Business Management -- 9 AI in Human Resource Management -- 9.1 Introduction to Human Resource Management -- 9.2 Artificial Intelligence in Human Resources -- 9.3 Applications of AI in Human Resources -- 9.3.1 Salary Prediction -- 9.3.2 Recruitment -- 9.3.3 Course Recommendation -- 9.3.4 Employee Attrition Prediction -- Exercises -- References -- 10 AI in Sales -- 10.1 Introduction to Sales -- 10.1.1 The Sales Cycle -- 10.2 Artificial Intelligence in Sales -- 10.3 Applications of AI in Sales -- 10.3.1 Lead Scoring -- 10.3.2 Sales Assistant Chatbot -- 10.3.3 Product Recommender Systems -- 10.3.4 Recommending via Pairwise Correlated Purchases -- Exercises -- References -- 11 AI in Marketing -- 11.1 Introduction to Marketing -- 11.1.1 Sales vs Marketing -- 11.2 Artificial Intelligence in Marketing -- 11.3 Applications of AI in Marketing -- 11.3.1 Customer Segmentation -- 11.3.2 Analyzing Brand Associations -- Exercises -- References -- 12 AI in Supply Chain Management -- 12.1 Introduction to Supply Chain Management -- 12.1.1 Supply Chain Definition -- 12.1.2 Types of Supply Chain Models -- 12.1.3 Bullwhip Effect -- 12.1.4 Causes of Variation in Orders -- 12.1.5 Reducing the Bullwhip Effect -- 12.2 Artificial Intelligence in Supply Chain Management -- 12.3 Applications of AI in Supply Chain Management. , 12.3.1 Demand Forecasting with Anomaly Detection -- 12.3.2 Quality Assurance -- 12.3.3 Estimating Delivery Time -- 12.3.4 Delivery Optimization -- Exercises -- References -- 13 AI in Operations Management -- 13.1 Introduction to Operations Management -- 13.1.1 Business Process Management -- 13.1.2 Six Sigma -- 13.1.3 Supply Chain Management (SCM) vs. Operations Management (OM) -- 13.2 Artificial Intelligence in Operations Management -- 13.3 Applications of AI in Operations -- 13.3.1 Root Cause Analysis for IT Operations -- 13.3.2 Predictive Maintenance -- 13.3.3 Process Automation -- Exercises -- References -- 14 AI in Corporate Finance -- 14.1 Introduction to Corporate Finance -- 14.2 Artificial Intelligence in Finance -- 14.3 Applications of AI in Corporate Finance -- 14.3.1 Default Prediction -- 14.3.2 Predicting Credit Card Fraud -- Exercises -- References -- 15 AI in Business Law -- 15.1 Introduction to Business Law -- 15.1.1 Types of Businesses -- 15.1.2 Types of Business Laws -- 15.2 Artificial Intelligence in Business Law -- 15.3 Applications of AI in Business Law -- 15.3.1 Legal Document Summarization -- 15.3.2 Contract Review Assistant -- 15.3.3 Legal Research Assistant -- Exercises -- References -- 16 AI in Business Strategy -- 16.1 Introduction to Business Strategy -- 16.1.1 Types of Business Strategies -- 16.1.2 Business Strategy Frameworks -- 16.1.3 Barriers to Entry -- 16.2 Artificial Intelligence in Business Strategy -- 16.3 Applications of AI in Business Strategy -- 16.3.1 Startup Acquisition -- 16.3.2 Identifying Closest Competitors -- 16.3.3 SWOT Analysis -- Exercises -- References -- Index.

Additional Edition: Print version: Teoh, Teik Toe Artificial Intelligence in Business Management Singapore : Springer,c2024

Language: English