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Azure Data Scientist Associate Certification Guide

You're reading from   Azure Data Scientist Associate Certification Guide A hands-on guide to machine learning in Azure and passing the Microsoft Certified DP-100 exam

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Product type Paperback
Published in Dec 2021
Publisher Packt
ISBN-13 9781800565005
Length 448 pages
Edition 1st Edition
Languages
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Authors (2):
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Andreas Botsikas Andreas Botsikas
Author Profile Icon Andreas Botsikas
Andreas Botsikas
Michael Hlobil Michael Hlobil
Author Profile Icon Michael Hlobil
Michael Hlobil
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Toc

Table of Contents (17) Chapters Close

Preface 1. Section 1: Starting your cloud-based data science journey
2. Chapter 1: An Overview of Modern Data Science FREE CHAPTER 3. Chapter 2: Deploying Azure Machine Learning Workspace Resources 4. Chapter 3: Azure Machine Learning Studio Components 5. Chapter 4: Configuring the Workspace 6. Section 2: No code data science experimentation
7. Chapter 5: Letting the Machines Do the Model Training 8. Chapter 6: Visual Model Training and Publishing 9. Section 3: Advanced data science tooling and capabilities
10. Chapter 7: The AzureML Python SDK 11. Chapter 8: Experimenting with Python Code 12. Chapter 9: Optimizing the ML Model 13. Chapter 10: Understanding Model Results 14. Chapter 11: Working with Pipelines 15. Chapter 12: Operationalizing Models with Code 16. Other Books You May Enjoy

Conventions used

There are a number of text conventions used throughout this book.

Code in text: Indicates code words in text, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and Twitter handles. Here is an example: "You can also change the autogenerated name of the pipeline you are designing. Rename the current pipeline to test-pipeline."

A block of code is set as follows:

from azureml.train.hyperdrive import GridParameterSampling
from azureml.train.hyperdrive import choice
param_sampling = GridParameterSampling( {
        "a": choice(0.01, 0.5),
        "b": choice(10, 100)
    }
)

When we wish to draw your attention to a particular part of a code block, the relevant lines or items are set in bold:

from azureml.core import Workspace
ws = Workspace.from_config()
loans_ds = ws.datasets['loans']
compute_target = ws.compute_targets['cpu-sm-cluster']

Any command-line input or output is written as follows:

az group create --name my-name-rg --location westeurope

Bold: Indicates a new term, an important word, or words that you see onscreen. For instance, words in menus or dialog boxes appear in bold. Here is an example: "Navigate to the Author | Notebooks section of your AzureML Studio web interface."

Tips or important notes

Run numbers may be different in your executions. Every time you execute the cells, a new run number is created, continuing from the previous number. So, if you execute code that performs one hyperdrive run with 20 child runs, the last child run will be run 21. The next time you execute the same code, the hyperdrive run will start from run 22, and the last child will be run 42. The run numbers referred to in this section are the ones shown in the various figures, and it is normal to observe differences, especially if you had to rerun a couple of cells.

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