Machine Learning for Business, Video Edition

Video description

In Video Editions the narrator reads the book while the content, figures, code listings, diagrams, and text appear on the screen. Like an audiobook that you can also watch as a video.

  • Imagine predicting which customers are thinking about switching to a competitor or flagging potential process failures before they happen
  • Think about the benefits of forecasting tedious business processes and back-office tasks
  • Envision quickly gauging customer sentiment from social media content (even large volumes of it).
  • Consider the competitive advantage of making decisions when you know the most likely future events
Machine learning can deliver these and other advantages to your business, and it’s never been easier to get started!

About the Technology
Machine learning can deliver huge benefits for everyday business tasks. With some guidance, you can get those big wins yourself without complex math or highly paid consultants! If you can crunch numbers in Excel, you can use modern ML services to efficiently direct marketing dollars, identify and keep your best customers, and optimize back office processes. This book shows you how.

About the Book
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.

What's Inside
  • Identifying tasks suited to machine learning
  • Automating back office processes
  • Using open source and cloud-based tools
  • Relevant case studies


About the Reader
For technically inclined business professionals or business application developers.

About the Authors
Doug Hudgeon and Richard Nichol specialize in maximizing the value of business data through AI and machine learning for companies of any size.

Quotes
A clear and well-explained set of practical examples that demonstrates how to solve everyday problems, suitable for technical and nontechnical readers alike.
- John Bassil, Fethr

Answers the question of how machine learning can help your company automate processes.
- James Black, Nissan North America

A great resource for introducing machine learning through real-world examples.
- Shawn Eion Smith, Penn State University

Makes AI accessible to the regular business owner.
- Dhivya Sivasubramanian, Science Logic

Table of contents

  1. Part 1. Machine learning for business
  2. Chapter 1. How machine learning applies to your business
  3. Chapter 1. Why is automation important now?
  4. Chapter 1. How do machines make decisions?
  5. Chapter 1. Can a machine help Karen make decisions?
  6. Chapter 1. How does a machine learn?
  7. Chapter 1. Getting approval in your company to use machine learning to make decisions
  8. Chapter 1. The tools
  9. Chapter 1. Setting up SageMaker in preparation for tackling the scenarios in- n chapters 2 through 7
  10. Chapter 1. The time to act is now
  11. Chapter 1. Summary
  12. Part 2. Six scenarios: Machine learning for business
  13. Chapter 2. Should you send a purchase order to a technical approver?
  14. Chapter 2. The data
  15. Chapter 2. Putting on your training wheels
  16. Chapter 2. Running the Jupyter notebook and making predictions
  17. Chapter 2. Deleting the endpoint and shutting down your notebook instance
  18. Chapter 2. Summary
  19. Chapter 3. Should you call a customer because they are at risk of churning?
  20. Chapter 3. The process flow
  21. Chapter 3. Preparing the dataset
  22. Chapter 3. XGBoost primer
  23. Chapter 3. Getting ready to build the model
  24. Chapter 3. Building the model
  25. Chapter 3. Deleting the endpoint and shutting down your notebook instance
  26. Chapter 3. Checking to make sure the endpoint is deleted
  27. Chapter 3. Summary
  28. Chapter 4. Should an incident be escalated to your support team?
  29. Chapter 4. The process flow
  30. Chapter 4. Preparing the dataset
  31. Chapter 4. NLP (natural language processing)
  32. Chapter 4. What is BlazingText and how does it work?
  33. Chapter 4. Getting ready to build the model
  34. Chapter 4. Building the model
  35. Chapter 4. Deleting the endpoint and shutting down your notebook instance
  36. Chapter 4. Checking to make sure the endpoint is deleted
  37. Chapter 4. Summary
  38. Chapter 5. Should you question an invoice sent by a supplier?
  39. Chapter 5. The process flow
  40. Chapter 5. Preparing the dataset
  41. Chapter 5. What are anomalies
  42. Chapter 5. Supervised vs. unsupervised machine learning
  43. Chapter 5. What is Random Cut Forest and how does it work?
  44. Chapter 5. Getting ready to build the model
  45. Chapter 5. Building the model
  46. Chapter 5. Deleting the endpoint and shutting down your notebook instance
  47. Chapter 5. Checking to make sure the endpoint is deleted
  48. Chapter 5. Summary
  49. Chapter 6. Forecasting your company’s monthly power usage
  50. Chapter 6. Loading the Jupyter notebook for working with time-series data
  51. Chapter 6. Preparing the dataset: Charting time-series data
  52. Chapter 6. What is a neural network?
  53. Chapter 6. Getting ready to build the model
  54. Chapter 6. Building the model
  55. Chapter 6. Deleting the endpoint and shutting down your notebook instance
  56. Chapter 6. Checking to make sure the endpoint is deleted
  57. Chapter 6. Summary
  58. Chapter 7. Improving your company’s monthly power usage forecast
  59. Chapter 7. DeepAR’s greatest strength: Incorporating related time series
  60. Chapter 7. Incorporating additional datasets into Kiara’s power consumption model
  61. Chapter 7. Getting ready to build the model
  62. Chapter 7. Building the model
  63. Chapter 7. Deleting the endpoint and shutting down your notebook instance
  64. Chapter 7. Checking to make sure the endpoint is deleted
  65. Chapter 7. Summary
  66. Part 3. Moving machine learning into production
  67. Chapter 8. Serving predictions over the web
  68. Chapter 8. Overview of steps for this chapter
  69. Chapter 8. The SageMaker endpoint
  70. Chapter 8. Setting up the SageMaker endpoint
  71. Chapter 8. Setting up the serverless API endpoint
  72. Chapter 8. Creating the web endpoint
  73. Chapter 8. Serving decisions
  74. Chapter 8. Summary
  75. Chapter 9. Case studies
  76. Chapter 9. Case study 2: Faethm
  77. Chapter 9. Conclusion
  78. Chapter 9. Summary
  79. Appendix A. Signing up for Amazon AWS
  80. Appendix A. AWS Billing overview
  81. Appendix B. Setting up and using S3 to store files
  82. Appendix B. Setting up folders in S3
  83. Appendix B. Uploading files to S3
  84. Appendix C. Setting up and using AWS SageMaker to build a machine learning system
  85. Appendix C. Starting at the Dashboard
  86. Appendix C. Creating a notebook instance
  87. Appendix C. Starting the notebook instance
  88. Appendix C. Uploading the notebook to the notebook instance
  89. Appendix C. Running the notebook
  90. Appendix D. Shutting it all down
  91. Appendix D. Shutting down the notebook instance
  92. Appendix E. Installing Python

Product information

  • Title: Machine Learning for Business, Video Edition
  • Author(s): Doug Hudgeon, Richard Nichol
  • Release date: January 2020
  • Publisher(s): Manning Publications
  • ISBN: None