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Automated Machine Learning

You're reading from   Automated Machine Learning Hyperparameter optimization, neural architecture search, and algorithm selection with cloud platforms

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Product type Paperback
Published in Feb 2021
Publisher Packt
ISBN-13 9781800567689
Length 312 pages
Edition 1st Edition
Languages
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Author (1):
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Adnan Masood Adnan Masood
Author Profile Icon Adnan Masood
Adnan Masood
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Table of Contents (15) Chapters Close

Preface 1. Section 1: Introduction to Automated Machine Learning
2. Chapter 1: A Lap around Automated Machine Learning FREE CHAPTER 3. Chapter 2: Automated Machine Learning, Algorithms, and Techniques 4. Chapter 3: Automated Machine Learning with Open Source Tools and Libraries 5. Section 2: AutoML with Cloud Platforms
6. Chapter 4: Getting Started with Azure Machine Learning 7. Chapter 5: Automated Machine Learning with Microsoft Azure 8. Chapter 6: Machine Learning with AWS 9. Chapter 7: Doing Automated Machine Learning with Amazon SageMaker Autopilot 10. Chapter 8: Machine Learning with Google Cloud Platform 11. Chapter 9: Automated Machine Learning with GCP 12. Section 3: Applied Automated Machine Learning
13. Chapter 10: AutoML in the Enterprise 14. Other Books You May Enjoy

Creating an Amazon SageMaker Autopilot limited experiment

Let's gets a hands-on introduction to applying AutoML using SageMaker Autopilot. We will download and apply AutoML to an open source dataset. Let's get started!

  1. From Amazon SageMaker Studio, start a data science notebook by clicking on the Python 3 button, as shown in the following screenshot:

    Figure 7.1 – Amazon SageMaker Launcher main screen

    Download the Bank Marketing dataset from UCI by calling the following URL retrieve commands and save it in your notebook:

    Figure 7.2 – Amazon SageMaker Studio Jupyter Notebook – downloading the dataset

    This Bank Marketing dataset is from a Portuguese banking institution and has the classification goal of predicting the client's subscription to deposit (binary feature, y). The dataset is from Moro, Cortez, and Rita's paper on "A Data-Driven Approach to Predict the Success of Bank Telemarketing. Decision Support Systems", published...

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