Cover image for Machine Learning for Business

Machine Learning for Business

Author Richard Nichol , Doug Hudgeon
Release Date: 2020/01/01
ISBN: 9781617295836
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Book Description

Machine Learning for Business teaches business-oriented machine learning techniques you can do yourself. Concentrating on practical topics like customer retention, forecasting, and back office processes, you’ll work through six projects that help you form an ML-for-business mindset. To guarantee your success, you’ll use the Amazon SageMaker ML service, which makes it a snap to turn your questions into results.

Table of Contents

  1. Copyright
  2. Brief Table of Contents
  3. Table of Contents
  4. Preface
  5. Acknowledgments
  6. About this book
  7. About the Author
  8. About the cover illustration
  9. Part 1. Machine learning for business
    1. Chapter 1. How machine learning applies to your business
      1. 1.1. Why are our business systems so terrible?
      2. 1.2. Why is automation important now?
      3. 1.3. How do machines make decisions?
      4. 1.4. Can a machine help Karen make decisions?
      5. 1.5. How does a machine learn?
      6. 1.6. Getting approval in your company to use machine learning to make decisions
      7. 1.7. The tools
      8. 1.8. Setting up SageMaker in preparation for tackling the scenarios in- n chapters 2 through 7
      9. 1.9. The time to act is now
      10. Summary
  10. Part 2. Six scenarios: Machine learning for business
    1. Chapter 2. Should you send a purchase order to a technical approver?
      1. 2.1. The decision
      2. 2.2. The data
      3. 2.3. Putting on your training wheels
      4. 2.4. Running the Jupyter notebook and making predictions
      5. 2.5. Deleting the endpoint and shutting down your notebook instance
      6. Summary
    2. Chapter 3. Should you call a customer because they are at risk of churning?
      1. 3.1. What are you making decisions about?
      2. 3.2. The process flow
      3. 3.3. Preparing the dataset
      4. 3.4. XGBoost primer
      5. 3.5. Getting ready to build the model
      6. 3.6. Building the model
      7. 3.7. Deleting the endpoint and shutting down your notebook instance
      8. 3.8. Checking to make sure the endpoint is deleted
      9. Summary
    3. Chapter 4. Should an incident be escalated to your support team?
      1. 4.1. What are you making decisions about?
      2. 4.2. The process flow
      3. 4.3. Preparing the dataset
      4. 4.4. NLP (natural language processing)
      5. 4.5. What is BlazingText and how does it work?
      6. 4.6. Getting ready to build the model
      7. 4.7. Building the model
      8. 4.8. Deleting the endpoint and shutting down your notebook instance
      9. 4.9. Checking to make sure the endpoint is deleted
      10. Summary
    4. Chapter 5. Should you question an invoice sent by a supplier?
      1. 5.1. What are you making decisions about?
      2. 5.2. The process flow
      3. 5.3. Preparing the dataset
      4. 5.4. What are anomalies
      5. 5.5. Supervised vs. unsupervised machine learning
      6. 5.6. What is Random Cut Forest and how does it work?
      7. 5.7. Getting ready to build the model
      8. 5.8. Building the model
      9. 5.9. Deleting the endpoint and shutting down your notebook instance
      10. 5.10. Checking to make sure the endpoint is deleted
      11. Summary
    5. Chapter 6. Forecasting your company’s monthly power usage
      1. 6.1. What are you making decisions about?
      2. 6.2. Loading the Jupyter notebook for working with time-series data
      3. 6.3. Preparing the dataset: Charting time-series data
      4. 6.4. What is a neural network?
      5. 6.5. Getting ready to build the model
      6. 6.6. Building the model
      7. 6.7. Deleting the endpoint and shutting down your notebook instance
      8. 6.8. Checking to make sure the endpoint is deleted
      9. Summary
    6. Chapter 7. Improving your company’s monthly power usage forecast
      1. 7.1. DeepAR’s ability to pick up periodic events
      2. 7.2. DeepAR’s greatest strength: Incorporating related time series
      3. 7.3. Incorporating additional datasets into Kiara’s power consumption model
      4. 7.4. Getting ready to build the model
      5. 7.5. Building the model
      6. 7.6. Deleting the endpoint and shutting down your notebook instance
      7. 7.7. Checking to make sure the endpoint is deleted
      8. Summary
  11. Part 3. Moving machine learning into production
    1. Chapter 8. Serving predictions over the web
      1. 8.1. Why is serving decisions and predictions over the web so difficult?
      2. 8.2. Overview of steps for this chapter
      3. 8.3. The SageMaker endpoint
      4. 8.4. Setting up the SageMaker endpoint
      5. 8.5. Setting up the serverless API endpoint
      6. 8.6. Creating the web endpoint
      7. 8.7. Serving decisions
      8. Summary
    2. Chapter 9. Case studies
      1. 9.1. Case study 1: WorkPac
      2. 9.2. Case study 2: Faethm
      3. 9.3. Conclusion
      4. Summary
  12. Appendix A. Signing up for Amazon AWS
    1. A.1 Signing up for AWS
    2. A.2 AWS Billing overview
  13. Appendix B. Setting up and using S3 to store files
    1. B.1 Creating and setting up a bucket in S3
    2. B.2 Setting up folders in S3
    3. B.3 Uploading files to S3
  14. Appendix C. Setting up and using AWS SageMaker to build a machine learning system
    1. C.1 Setting up
    2. C.2 Starting at the Dashboard
    3. C.3 Creating a notebook instance
    4. C.4 Starting the notebook instance
    5. C.5 Uploading the notebook to the notebook instance
    6. C.6 Running the notebook
  15. Appendix D. Shutting it all down
    1. D.1 Deleting the endpoint
    2. D.2 Shutting down the notebook instance
  16. Appendix E. Installing Python
  17. Index
  18. List of Figures
  19. List of Tables
  20. List of Listings
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