book

Machine Learning for Business

Name: Machine Learning for Business
ISBN: 9781617295836

by Richard Nichol, Doug Hudgeon

January 2020

Intermediate to advanced

280 pages

9h 22m

English

Manning Publications

Audiobook available

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Copyright
Brief Table of Contents
Table of Contents
Preface
Acknowledgments
About this book
About the Author
About the cover illustration
Part 1. Machine learning for business
Chapter 1. How machine learning applies to your business
1.1. Why are our business systems so terrible?1.2. Why is automation important now?1.3. How do machines make decisions?1.4. Can a machine help Karen make decisions?1.5. How does a machine learn?1.6. Getting approval in your company to use machine learning to make decisions1.7. The tools1.8. Setting up SageMaker in preparation for tackling the scenarios in- n chapters 2 through 71.9. The time to act is nowSummary

Part 2. Six scenarios: Machine learning for business
Chapter 2. Should you send a purchase order to a technical approver?
2.1. The decision2.2. The data2.3. Putting on your training wheels2.4. Running the Jupyter notebook and making predictions2.5. Deleting the endpoint and shutting down your notebook instanceSummary
Chapter 3. Should you call a customer because they are at risk of churning?
3.1. What are you making decisions about?3.2. The process flow3.3. Preparing the dataset3.4. XGBoost primer3.5. Getting ready to build the model3.6. Building the model3.7. Deleting the endpoint and shutting down your notebook instance3.8. Checking to make sure the endpoint is deletedSummary
Chapter 4. Should an incident be escalated to your support team?
4.1. What are you making decisions about?4.2. The process flow4.3. Preparing the dataset4.4. NLP (natural language processing)4.5. What is BlazingText and how does it work?4.6. Getting ready to build the model4.7. Building the model4.8. Deleting the endpoint and shutting down your notebook instance4.9. Checking to make sure the endpoint is deletedSummary
Chapter 5. Should you question an invoice sent by a supplier?
5.1. What are you making decisions about?5.2. The process flow5.3. Preparing the dataset5.4. What are anomalies5.5. Supervised vs. unsupervised machine learning5.6. What is Random Cut Forest and how does it work?5.7. Getting ready to build the model5.8. Building the model5.9. Deleting the endpoint and shutting down your notebook instance5.10. Checking to make sure the endpoint is deletedSummary
Chapter 6. Forecasting your company’s monthly power usage
6.1. What are you making decisions about?6.2. Loading the Jupyter notebook for working with time-series data6.3. Preparing the dataset: Charting time-series data6.4. What is a neural network?6.5. Getting ready to build the model6.6. Building the model6.7. Deleting the endpoint and shutting down your notebook instance6.8. Checking to make sure the endpoint is deletedSummary
Chapter 7. Improving your company’s monthly power usage forecast
7.1. DeepAR’s ability to pick up periodic events7.2. DeepAR’s greatest strength: Incorporating related time series7.3. Incorporating additional datasets into Kiara’s power consumption model7.4. Getting ready to build the model7.5. Building the model7.6. Deleting the endpoint and shutting down your notebook instance7.7. Checking to make sure the endpoint is deletedSummary
Part 3. Moving machine learning into production
Chapter 8. Serving predictions over the web
8.1. Why is serving decisions and predictions over the web so difficult?8.2. Overview of steps for this chapter8.3. The SageMaker endpoint8.4. Setting up the SageMaker endpoint8.5. Setting up the serverless API endpoint8.6. Creating the web endpoint8.7. Serving decisionsSummary
Chapter 9. Case studies
9.1. Case study 1: WorkPac9.2. Case study 2: Faethm9.3. ConclusionSummary
Appendix A. Signing up for Amazon AWS
A.1 Signing up for AWS
A.2 AWS Billing overview
Appendix B. Setting up and using S3 to store files
B.1 Creating and setting up a bucket in S3
B.2 Setting up folders in S3
B.3 Uploading files to S3
Appendix C. Setting up and using AWS SageMaker to build a machine learning system
C.1 Setting up
C.2 Starting at the Dashboard
C.3 Creating a notebook instance
C.4 Starting the notebook instance
C.5 Uploading the notebook to the notebook instance
C.6 Running the notebook
Appendix D. Shutting it all down
D.1 Deleting the endpoint
D.2 Shutting down the notebook instance
Appendix E. Installing Python
Index
List of Figures
List of Tables
List of Listings

Content preview from Machine Learning for Business

List of Tables

Chapter 2. Should you send a purchase order to a technical approver?

Table 2.1. Technical Approval Required dataset contains information from prior orders received by Karen.

Table 2.2. Technical Approval Required dataset displayed in Excel

Table 2.3. Simple three-row dataset with prices for a desk, a keyboard, and a mouse

Table 2.4. The three-row dataset after applying the get_dummies function

Table 2.5. Correlation with predicted value

Table 2.6. Test results from the predictor

Chapter 3. Should you call a customer because they are at risk of churning?

Table 3.1. Test results from the predictor

Table 3.2. Customer dataset grouped by week after normalizing the data

Table 3.3. Customer dataset grouped by week, showing changes ...

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Machine Learning for Business

by Richard Nichol, Doug Hudgeon

List of Tables

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