Machine Learning for Business

Summary

• 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 author

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

Table of Contents:

PART 1 MACHINE LEARNING FOR BUSINESS

1 ¦ How machine learning applies to your business

PART 2 SIX SCENARIOS: MACHINE LEARNING FOR BUSINESS

2 ¦ Should you send a purchase order to a technical approver?

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

4 ¦ Should an incident be escalated to your support team?

5 ¦ Should you question an invoice sent by a supplier?

6 ¦ Forecasting your company’s monthly power usage

7 ¦ Improving your company’s monthly power usage forecast

PART 3 MOVING MACHINE LEARNING INTO PRODUCTION

8 ¦ Serving predictions over the web

9 ¦ Case studies