feed icon rss

Your email was sent successfully. Check your inbox.

An error occurred while sending the email. Please try again.

Proceed reservation?

Export
Filter
Type of Medium
Language
Region
Years
Subjects(RVK)
Access
  • 1
    UID:
    almahu_9948104408102882
    Format: XVIII, 379 p. 80 illus. , online resource.
    ISBN: 9781484237878
    Content: Gain practical skills in machine learning for finance, healthcare, and retail. This book uses a hands-on approach by providing case studies from each of these domains: you’ll see examples that demonstrate how to use machine learning as a tool for business enhancement. As a domain expert, you will not only discover how machine learning is used in finance, healthcare, and retail, but also work through practical case studies where machine learning has been implemented. Machine Learning Applications Using Python is divided into three sections, one for each of the domains (healthcare, finance, and retail). Each section starts with an overview of machine learning and key technological advancements in that domain. You’ll then learn more by using case studies on how organizations are changing the game in their chosen markets. This book has practical case studies with Python code and domain-specific innovative ideas for monetizing machine learning. You will: Discover applied machine learning processes and principles Implement machine learning in areas of healthcare, finance, and retail Avoid the pitfalls of implementing applied machine learning Build Python machine learning examples in the three subject areas.
    Note: Chapter 1. Overview of machine learning in healthcare -- Chapter 2. Key technological advancements in healthcare -- Chapter 3. How to implement machine learning in healthcare -- Chapter 4. Case studies on how organizations are changing the game in the market -- Chapter 5. Pitfalls to avoid while implementing machine learning in healthcare -- Chapter 6. Healthcare specific innovative Ideas for monetizing machine learning -- Chapter 7. Overview of machine learning in retail -- Chapter 8. Key technological advancements in retail -- Chapter 9. How to implement machine learning in retail -- Chapter 10. Case studies on how organizations are changing the game in the market -- Chapter 11. Pitfalls to avoid while implementing machine learning in retail -- Chapter 12. Retail specific innovative Ideas for monetizing machine learning -- Chapter 13. Overview of machine learning in finance -- Chapter 14. Key technological advancements in finance -- Chapter 15. How to implement machine learning in finance -- Chapter 16. Case studies on how organizations are changing the game in the market -- Chapter 17. Pitfalls to avoid while implementing machine learning in finance -- Chapter 18. Finance specific innovative Ideas for monetizing machine learning. .
    In: Springer eBooks
    Additional Edition: Printed edition: ISBN 9781484237861
    Additional Edition: Printed edition: ISBN 9781484237885
    Additional Edition: Printed edition: ISBN 9781484247143
    Language: English
    Subjects: Computer Science
    RVK:
    RVK:
    URL: Volltext  (URL des Erstveröffentlichers)
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
  • 2
    UID:
    b3kat_BV046747929
    Format: 1 Online-Ressource (XV, 278 Seiten) , Illustrationen
    ISBN: 9781484255490
    Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 978-1-4842-5548-3
    Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 978-1-4842-5550-6
    Language: English
    Keywords: Maschinelles Lernen ; Python ; Internet der Dinge ; Raspberry Pi ; Arduino Micro ; Programmierung
    URL: Volltext  (URL des Erstveröffentlichers)
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
  • 3
    UID:
    almahu_BV046344495
    Format: XVIII, 379 Seiten : , Diagramme.
    ISBN: 978-1-4842-3786-1
    Series Statement: For professionals by professionals
    Note: Auf der Coverrückseite: "Shelve in: Databases / general, User level: Intermediate - advanced
    Additional Edition: Erscheint auch als Online-Ausgabe ISBN 978-1-4842-3787-8
    Language: English
    Subjects: Computer Science
    RVK:
    RVK:
    Keywords: Maschinelles Lernen ; Python
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
  • 4
    UID:
    almahu_9948351869202882
    Format: XV, 278 p. 105 illus. , online resource.
    Edition: 1st ed. 2020.
    ISBN: 9781484255490
    Content: Apply machine learning using the Internet of Things (IoT) in the agriculture, telecom, and energy domains with case studies. This book begins by covering how to set up the software and hardware components including the various sensors to implement the case studies in Python. The case study section starts with an examination of call drop with IoT in the telecoms industry, followed by a case study on energy audit and predictive maintenance for an industrial machine, and finally covers techniques to predict cash crop failure in agribusiness. The last section covers pitfalls to avoid while implementing machine learning and IoT in these domains. After reading this book, you will know how IoT and machine learning are used in the example domains and have practical case studies to use and extend. You will be able to create enterprise-scale applications using Raspberry Pi 3 B+ and Arduino Mega 2560 with Python. You will: Implement machine learning with IoT and solve problems in the telecom, agriculture, and energy sectors with Python Set up and use industrial-grade IoT products, such as Modbus RS485 protocol devices, in practical scenarios Develop solutions for commercial-grade IoT or IIoT projects Implement case studies in machine learning with IoT from scratch.
    Note: CHAPTER 1: Getting Started: Software and Hardware Needed -- CHAPTER 2: Overview of IoT and IIoT -- CHAPTER 3: Using Machine Learning with IoT and IIoT in Python -- CHAPTER 4: Using Machine Learning and IoT in Telecom, Energy, and Agriculture -- CHAPTER 5: Preparing for the Case Studies -- CHAPTER 6: Configuring IIoT Energy Meter -- CHAPTER 7: Telecom Industry Case Study: Solving the Problem of Call Drops with IoT -- CHAPTER 8: Energy Industry Case Study: Predictive Maintenance for an Industrial Machine -- CHAPTER 9: Agriculture Industry Case Study: Predicting a Cash Crop Yield. .
    In: Springer eBooks
    Additional Edition: Printed edition: ISBN 9781484255483
    Additional Edition: Printed edition: ISBN 9781484255506
    Language: English
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
  • 5
    UID:
    edocfu_9959013957502883
    Format: 1 online resource (384 pages)
    Edition: 1st ed. 2019.
    ISBN: 1-4842-3787-0
    Content: Gain practical skills in machine learning for finance, healthcare, and retail. This book uses a hands-on approach by providing case studies from each of these domains: you’ll see examples that demonstrate how to use machine learning as a tool for business enhancement. As a domain expert, you will not only discover how machine learning is used in finance, healthcare, and retail, but also work through practical case studies where machine learning has been implemented. Machine Learning Applications Using Python is divided into three sections, one for each of the domains (healthcare, finance, and retail). Each section starts with an overview of machine learning and key technological advancements in that domain. You’ll then learn more by using case studies on how organizations are changing the game in their chosen markets. This book has practical case studies with Python code and domain-specific innovative ideas for monetizing machine learning. You will: Discover applied machine learning processes and principles Implement machine learning in areas of healthcare, finance, and retail Avoid the pitfalls of implementing applied machine learning Build Python machine learning examples in the three subject areas.
    Note: Chapter 1. Overview of machine learning in healthcare -- Chapter 2. Key technological advancements in healthcare -- Chapter 3. How to implement machine learning in healthcare -- Chapter 4. Case studies on how organizations are changing the game in the market -- Chapter 5. Pitfalls to avoid while implementing machine learning in healthcare -- Chapter 6. Healthcare specific innovative Ideas for monetizing machine learning -- Chapter 7. Overview of machine learning in retail -- Chapter 8. Key technological advancements in retail -- Chapter 9. How to implement machine learning in retail -- Chapter 10. Case studies on how organizations are changing the game in the market -- Chapter 11. Pitfalls to avoid while implementing machine learning in retail -- Chapter 12. Retail specific innovative Ideas for monetizing machine learning -- Chapter 13. Overview of machine learning in finance -- Chapter 14. Key technological advancements in finance -- Chapter 15. How to implement machine learning in finance -- Chapter 16. Case studies on how organizations are changing the game in the market -- Chapter 17. Pitfalls to avoid while implementing machine learning in finance -- Chapter 18. Finance specific innovative Ideas for monetizing machine learning. .
    Additional Edition: ISBN 1-4842-3786-2
    Language: English
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
  • 6
    UID:
    edocfu_9959768571402883
    Format: 1 online resource (XV, 278 p. 105 illus.)
    Edition: 1st ed. 2020.
    Content: Apply machine learning using the Internet of Things (IoT) in the agriculture, telecom, and energy domains with case studies. This book begins by covering how to set up the software and hardware components including the various sensors to implement the case studies in Python. The case study section starts with an examination of call drop with IoT in the telecoms industry, followed by a case study on energy audit and predictive maintenance for an industrial machine, and finally covers techniques to predict cash crop failure in agribusiness. The last section covers pitfalls to avoid while implementing machine learning and IoT in these domains. After reading this book, you will know how IoT and machine learning are used in the example domains and have practical case studies to use and extend. You will be able to create enterprise-scale applications using Raspberry Pi 3 B+ and Arduino Mega 2560 with Python. You will: Implement machine learning with IoT and solve problems in the telecom, agriculture, and energy sectors with Python Set up and use industrial-grade IoT products, such as Modbus RS485 protocol devices, in practical scenarios Develop solutions for commercial-grade IoT or IIoT projects Implement case studies in machine learning with IoT from scratch.
    Note: Includes index. , CHAPTER 1: Getting Started: Software and Hardware Needed -- CHAPTER 2: Overview of IoT and IIoT -- CHAPTER 3: Using Machine Learning with IoT and IIoT in Python -- CHAPTER 4: Using Machine Learning and IoT in Telecom, Energy, and Agriculture -- CHAPTER 5: Preparing for the Case Studies -- CHAPTER 6: Configuring IIoT Energy Meter -- CHAPTER 7: Telecom Industry Case Study: Solving the Problem of Call Drops with IoT -- CHAPTER 8: Energy Industry Case Study: Predictive Maintenance for an Industrial Machine -- CHAPTER 9: Agriculture Industry Case Study: Predicting a Cash Crop Yield. .
    Additional Edition: ISBN 1-4842-5548-8
    Additional Edition: ISBN 1-4842-5549-6
    Language: English
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
  • 7
    UID:
    almafu_9959768571402883
    Format: 1 online resource (XV, 278 p. 105 illus.)
    Edition: 1st ed. 2020.
    Content: Apply machine learning using the Internet of Things (IoT) in the agriculture, telecom, and energy domains with case studies. This book begins by covering how to set up the software and hardware components including the various sensors to implement the case studies in Python. The case study section starts with an examination of call drop with IoT in the telecoms industry, followed by a case study on energy audit and predictive maintenance for an industrial machine, and finally covers techniques to predict cash crop failure in agribusiness. The last section covers pitfalls to avoid while implementing machine learning and IoT in these domains. After reading this book, you will know how IoT and machine learning are used in the example domains and have practical case studies to use and extend. You will be able to create enterprise-scale applications using Raspberry Pi 3 B+ and Arduino Mega 2560 with Python. You will: Implement machine learning with IoT and solve problems in the telecom, agriculture, and energy sectors with Python Set up and use industrial-grade IoT products, such as Modbus RS485 protocol devices, in practical scenarios Develop solutions for commercial-grade IoT or IIoT projects Implement case studies in machine learning with IoT from scratch.
    Note: Includes index. , CHAPTER 1: Getting Started: Software and Hardware Needed -- CHAPTER 2: Overview of IoT and IIoT -- CHAPTER 3: Using Machine Learning with IoT and IIoT in Python -- CHAPTER 4: Using Machine Learning and IoT in Telecom, Energy, and Agriculture -- CHAPTER 5: Preparing for the Case Studies -- CHAPTER 6: Configuring IIoT Energy Meter -- CHAPTER 7: Telecom Industry Case Study: Solving the Problem of Call Drops with IoT -- CHAPTER 8: Energy Industry Case Study: Predictive Maintenance for an Industrial Machine -- CHAPTER 9: Agriculture Industry Case Study: Predicting a Cash Crop Yield. .
    Additional Edition: ISBN 1-4842-5548-8
    Additional Edition: ISBN 1-4842-5549-6
    Language: English
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
  • 8
    UID:
    almafu_9959013957502883
    Format: 1 online resource (384 pages)
    Edition: 1st ed. 2019.
    ISBN: 1-4842-3787-0
    Content: Gain practical skills in machine learning for finance, healthcare, and retail. This book uses a hands-on approach by providing case studies from each of these domains: you’ll see examples that demonstrate how to use machine learning as a tool for business enhancement. As a domain expert, you will not only discover how machine learning is used in finance, healthcare, and retail, but also work through practical case studies where machine learning has been implemented. Machine Learning Applications Using Python is divided into three sections, one for each of the domains (healthcare, finance, and retail). Each section starts with an overview of machine learning and key technological advancements in that domain. You’ll then learn more by using case studies on how organizations are changing the game in their chosen markets. This book has practical case studies with Python code and domain-specific innovative ideas for monetizing machine learning. You will: Discover applied machine learning processes and principles Implement machine learning in areas of healthcare, finance, and retail Avoid the pitfalls of implementing applied machine learning Build Python machine learning examples in the three subject areas.
    Note: Chapter 1. Overview of machine learning in healthcare -- Chapter 2. Key technological advancements in healthcare -- Chapter 3. How to implement machine learning in healthcare -- Chapter 4. Case studies on how organizations are changing the game in the market -- Chapter 5. Pitfalls to avoid while implementing machine learning in healthcare -- Chapter 6. Healthcare specific innovative Ideas for monetizing machine learning -- Chapter 7. Overview of machine learning in retail -- Chapter 8. Key technological advancements in retail -- Chapter 9. How to implement machine learning in retail -- Chapter 10. Case studies on how organizations are changing the game in the market -- Chapter 11. Pitfalls to avoid while implementing machine learning in retail -- Chapter 12. Retail specific innovative Ideas for monetizing machine learning -- Chapter 13. Overview of machine learning in finance -- Chapter 14. Key technological advancements in finance -- Chapter 15. How to implement machine learning in finance -- Chapter 16. Case studies on how organizations are changing the game in the market -- Chapter 17. Pitfalls to avoid while implementing machine learning in finance -- Chapter 18. Finance specific innovative Ideas for monetizing machine learning. .
    Additional Edition: ISBN 1-4842-3786-2
    Language: English
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
Close ⊗
This website uses cookies and the analysis tool Matomo. Further information can be found on the KOBV privacy pages