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Practical Automated Machine Learning Using H2O.ai

You're reading from   Practical Automated Machine Learning Using H2O.ai Discover the power of automated machine learning, from experimentation through to deployment to production

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Product type Paperback
Published in Sep 2022
Publisher Packt
ISBN-13 9781801074520
Length 396 pages
Edition 1st Edition
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Author (1):
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Salil Ajgaonkar Salil Ajgaonkar
Author Profile Icon Salil Ajgaonkar
Salil Ajgaonkar
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Toc

Table of Contents (19) Chapters Close

Preface 1. Part 1 H2O AutoML Basics
2. Chapter 1: Understanding H2O AutoML Basics FREE CHAPTER 3. Chapter 2: Working with H2O Flow (H2O’s Web UI) 4. Part 2 H2O AutoML Deep Dive
5. Chapter 3: Understanding Data Processing 6. Chapter 4: Understanding H2O AutoML Architecture and Training 7. Chapter 5: Understanding AutoML Algorithms 8. Chapter 6: Understanding H2O AutoML Leaderboard and Other Performance Metrics 9. Chapter 7: Working with Model Explainability 10. Part 3 H2O AutoML Advanced Implementation and Productization
11. Chapter 8: Exploring Optional Parameters for H2O AutoML 12. Chapter 9: Exploring Miscellaneous Features in H2O AutoML 13. Chapter 10: Working with Plain Old Java Objects (POJOs) 14. Chapter 11: Working with Model Object, Optimized (MOJO) 15. Chapter 12: Working with H2O AutoML and Apache Spark 16. Chapter 13: Using H2O AutoML with Other Technologies 17. Index 18. Other Books You May Enjoy

Understanding AutoML Algorithms

All ML algorithms have a foundation in computational statistics. Computational statistics is the combination of statistics and computer science where computers are used to compute complex mathematics. This computation is the ML algorithm and the results that we get from it are the predictions. As engineers and scientists working in the field of ML, we are often expected to know the basic logic of ML algorithms. There are plenty of ML algorithms in the AI domain. All of them aim to solve different types of prediction problems. All of them also have their own set of pros and cons. Thus, it became the job of engineers and scientists to find the best ML algorithms that can solve a given prediction problem within the required constraints. This job, however, was eased with the invention of AutoML.

Despite AutoML taking over this huge responsibility of finding the best ML algorithm, it is still our job as engineers and scientists to verify and justify the...

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