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Hands-On Automated Machine Learning

You're reading from   Hands-On Automated Machine Learning A beginner's guide to building automated machine learning systems using AutoML and Python

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Product type Paperback
Published in Apr 2018
Publisher Packt
ISBN-13 9781788629898
Length 282 pages
Edition 1st Edition
Languages
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Authors (2):
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 Das Das
Author Profile Icon Das
Das
 Mert Cakmak Mert Cakmak
Author Profile Icon Mert Cakmak
Mert Cakmak
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Toc

Table of Contents (15) Chapters Close

Title Page
Copyright and Credits
Packt Upsell
Contributors
Preface
1. Introduction to AutoML FREE CHAPTER 2. Introduction to Machine Learning Using Python 3. Data Preprocessing 4. Automated Algorithm Selection 5. Hyperparameter Optimization 6. Creating AutoML Pipelines 7. Dive into Deep Learning 8. Critical Aspects of ML and Data Science Projects 1. Other Books You May Enjoy Index

Appendix 1. Other Books You May Enjoy

If you enjoyed this book, you may be interested in these other books by Packt:

Python Machine Learning - Second Edition Sebastian Raschka, Vahid Mirjalili

ISBN: 978-1-78712-593-3

  • Understand the key frameworks in data science, machine learning, and deep learning
  • Harness the power of the latest Python open source libraries in machine learning
  • Master machine learning techniques using challenging real-world data
  • Master deep neural network implementation using the TensorFlow library
  • Ask new questions of your data through machine learning models and neural networks
  • Learn the mechanics of classification algorithms to implement the best tool for the job
  • Predict continuous target outcomes using regression analysis
  • Uncover hidden patterns and structures in data with clustering
  • Delve deeper into textual and social media data using sentiment analysis

Machine Learning with TensorFlow 1.x Quan Hua, Shams Ul Azeem, Saif Ahmed

ISBN: 978-1-78646-296-1

  • Explore how to use different machine learning models to ask different questions of your data
  • Learn how to build deep neural networks using TensorFlow 1.x
  • Cover key tasks such as clustering, sentiment analysis, and regression analysis using TensorFlow 1.x
  • Find out how to write clean and elegant Python code that will optimize the strength of your algorithms
  • Discover how to embed your machine learning model in a web application for increased accessibility
  • Learn how to use multiple GPUs for faster training using AWS
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