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1、Big Book ofMachine learning use cases 03EditionCHAPTER 1:Introduction 3CHAPTER 2:Moneyball 2.0:Improving Pitch-by-Pitch Decision-Making With MLBs Statcast Data 4CHAPTER 3:Improving On-Shelf Availability for Items With Out-of-Stock Modeling 14CHAPTER 4:Using Dynamic Time Warping and MLflow to Detect

2、Sales Trends Part 1:Understanding Dynamic Time Warping 20Part 2:Using Dynamic Time Warping and MLflow to Detect Sales Trends 26CHAPTER 5:Detecting Financial Fraud at Scale With Decision Trees and MLflow on Databricks 34CHAPTER 6:AI Drug Discovery Made Easy:Your Guide to Chemprop on Databricks 45CHAP

3、TER 7:Efficient Distributed Energy Load Forecasting with Databricks at EDP E-REDES 53CHAPTER 8:Processing Geospatial Data at Scale With Databricks 63Contents2E B O O K:B I G B O O K O F M AC H I N E L E A R N I N G U S E CAS E S 3 R D E D I T I O NOrganizations across many industries are using machi

4、ne learning to power new customer experiences,optimize business processes and improve employee productivity.From detecting financial fraud to improving the play-by-play decision-making for professional sports teams,this book brings together a multitude of practical use cases to get you started on yo

5、ur machine learning journey.The collection also serves as a guide including code samples and notebooks so you can roll up your sleeves and dive into machine learning on the Databricks Lakehouse.CHAPTER 1:Introduction3E B O O K:B I G B O O K O F M AC H I N E L E A R N I N G U S E CAS E S 3 R D E D I

6、T I O NIntroductionThe Oakland Athletics baseball team in 2002 used data analysis and quantitative modeling to identify undervalued players and create a competitive lineup on a limited budget.The book“Moneyball,”written by Michael Lewis,highlighted the As 02 season and gave an inside glimpse into ho