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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

Product type Paperback

Published in Dec 2021

Publisher Packt

ISBN-13 9781801076807

Length 344 pages

Edition 1st Edition

Languages

Python

Tools

Datarobot

Concepts

Agile

Authors (2):

Bipin Chadha

Sylvester Juwe

View More author details

Table of Contents (19) Chapters

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

Technical requirements

For the analysis and modeling that will be carried out in this chapter, you will need access to the DataRobot software. Jupyter Notebook is crucial for this chapter as most of the interactions with DataRobot will be carried out from the console. Your Python version should be 2.7 or 3.4+. Now, let's look at the dataset that will be utilized in this chapter.

Check out the following video to see the Code in Action at https://bit.ly/3wV4qx5.

Automobile Dataset

The automobile dataset can be accessed at the UCI Machine Learning Repository ( https://archive.ics.uci.edu/ml/datasets/Automobile). Each row in this dataset represents a specific automobile. The features (columns) describe its characteristics, risk rating, and associated normalized losses. Even though it is a small dataset, it has many features that are numerical as well as categorical. Its features are described on its web page and the data is provided in.csv format.

Dataset Citation

Dua...

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Authors (2)

Chadha

Chadha

Bipin Chadha is a hands-on leader of data science teams who can find innovative solutions to complex problems. He creates systemic data-driven models that enable executives to understand how their business operates, analyze a broad range of scenarios and strategies, and understand the likely implications of decisions and events prior to implementing risky changes. His passion is building data-driven cultures, developing effective teams, and driving organizations to grow and succeed.

See other products by Chadha

Juwe

Juwe

Sylvester Juwe is a highly accomplished Executive, with hands-on technical expertise implementing complex Big Data and Advanced Analytics solutions, from conceptualisation to commercial impact. He is a well versed leader who leverages sophisticated data capabilities, influences stakeholders, and creates a strong culture of governance and curiosity in solving complex business challenges, thereby creating commercial impact.

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