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Agile Machine Learning with DataRobot

You're reading from   Agile Machine Learning with DataRobot Automate each step of the machine learning life cycle, from understanding problems to delivering value

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Product type Paperback
Published in Dec 2021
Publisher Packt
ISBN-13 9781801076807
Length 344 pages
Edition 1st Edition
Languages
Concepts
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Authors (2):
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Bipin Chadha Bipin Chadha
Author Profile Icon Bipin Chadha
Bipin Chadha
Sylvester Juwe Sylvester Juwe
Author Profile Icon Sylvester Juwe
Sylvester Juwe
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Toc

Table of Contents (19) Chapters Close

Preface 1. Section 1: Foundations
2. Chapter 1: What Is DataRobot and Why You Need It? FREE CHAPTER 3. Chapter 2: Machine Learning Basics 4. Chapter 3: Understanding and Defining Business Problems 5. Section 2: Full ML Life Cycle with DataRobot: Concept to Value
6. Chapter 4: Preparing Data for DataRobot 7. Chapter 5: Exploratory Data Analysis with DataRobot 8. Chapter 6: Model Building with DataRobot 9. Chapter 7: Model Understanding and Explainability 10. Chapter 8: Model Scoring and Deployment 11. Section 3: Advanced Topics
12. Chapter 9: Forecasting and Time Series Modeling 13. Chapter 10: Recommender Systems 14. Chapter 11: Working with Geospatial Data, NLP, and Image Processing 15. Chapter 12: DataRobot Python API 16. Chapter 13: Model Governance and MLOps 17. Chapter 14: Conclusion 18. Other Books You May Enjoy

A conceptual introduction to recommender systems

Businesses have a long-standing history of recommending their products or services to customers. For instance, walk into a bookshop and you are likely to see a list of popular books bought by other customers. This is a simple kind of recommendation system, as it gives buyers a snapshot of potential products to purchase.

In a bid to win in the digital economy, businesses are becoming increasingly customer-centric. Customer centricity implies that companies aim to put the needs of the customer first. Still, with the needs of customers being as diverse as the customers themselves, businesses need to take a unique approach in putting forward their products. This explains, in part, the failings of popularity-based recommendation systems, as they fail to consider the unique profiles of buyers. As such, with growing digitalization, increased business offerings, and a growing diversity of customers' needs, this approach is unlikely to...

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