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Monetizing machine learning : quickly turn Python ML ideas into web applications on the serverless cloud

Author: Manuel Amunategui; Mehdi Roopaei
Publisher: [New York] : Apress, [2018]
Edition/Format:   eBook : Document : EnglishView all editions and formats
Summary:
Chapter 2: Client-Side Intelligence Using Regression Coefficients on Azure; Understanding Bike Rental Demand with Regression Coefficients; Exploring the Bike Sharing Dataset; Downloading the Data from the UCI Machine Learning Repository; Working with Jupyter Notebooks; Exploring the Data; A Closer Look at Our Outcome Variable; Quantitative Features vs. Rental Counts; Let's Look at Categorical Features; Preparing the  Read more...
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Genre/Form: Electronic books
Additional Physical Format: (OCoLC)1043880237
Material Type: Document, Internet resource
Document Type: Internet Resource, Computer File
All Authors / Contributors: Manuel Amunategui; Mehdi Roopaei
ISBN: 9781484238738 1484238737
OCLC Number: 1052766455
Description: 1 online resource.
Contents: Intro; Table of Contents; About the Authors; About the Technical Reviewers; Acknowledgments; Introduction; Chapter 1: Introduction to Serverless Technologies; A Simple Local Flask Application; Step 1: Basic "Hello World!" Example; Step 2: Start a Virtual Environment; Step 3: Install Flask; Step 4: Run Web Application; Step 5: View in Browser; Step 6: A Slightly Faster Way; Step 7: Closing It All Down; Introducing Serverless Hosting on Microsoft Azure; Step 1: Get an Account on Microsoft Azure; Step 2: Download Source Files; Supporting Files; Step 3: Install Git; Step 4: Open Azure Cloud Shell Step 5: Create a Deployment UserStep 6: Create a Resource Group; Step 7: Create an Azure Service Plan; Step 8: Create a Web App; Check Your Website Placeholder; Step 9: Pushing Out the Web Application; Step 10: View in Browser; Step 11: Don't Forget to Delete Your Web Application!; Conclusion and Additional Information; Introducing Serverless Hosting on Google Cloud; Step 1: Get an Account on Google Cloud; Step 2: Download Source Files; Step 3: Open Google Cloud Shell; Step 4: Upload Flask Files to Google Cloud; Step 5: Deploy Your Web Application on Google Cloud Step 6: Don't Forget to Delete Your Web Application!Conclusion and Additional Information; Introducing Serverless Hosting on Amazon AWS; Step 1: Get an Account on Amazon AWS; Step 2: Download Source Files; Step 3: Create an Access Account for Elastic Beanstalk; Step 4: Install Elastic Beanstalk (EB); Step 5: EB Command Line Interface; Step 6: Take if for a Spin; Step 7: Don't Forget to Turn It Off!; Conclusion and Additional Information; Introducing Hosting on PythonAnywhere; Step 1: Get an Account on PythonAnywhere; Step 2: Set Up Flask Web Framework; Conclusion and Additional Information Creating Dummy Features from Categorical DataTrying a Nonlinear Model; Even More Complex Feature Engineering-Leveraging Time-Series; A Parsimonious Model; Extracting Regression Coefficients from a Simple Model-an Easy Way to Predict Demand without Server-Side Computing; R-Squared; Predicting on New Data Using Extracted Coefficients; Designing a Fun and Interactive Web Application to Illustrate Bike Rental Demand; Abstracting Code for Readability and Extendibility; Building a Local Flask Application; Downloading and Running the Bike Sharing GitHub Code Locally; Debugging Tips
Responsibility: Manuel Amunategui, Mehdi Roopaei.

Abstract:

Chapter 2: Client-Side Intelligence Using Regression Coefficients on Azure; Understanding Bike Rental Demand with Regression Coefficients; Exploring the Bike Sharing Dataset; Downloading the Data from the UCI Machine Learning Repository; Working with Jupyter Notebooks; Exploring the Data; A Closer Look at Our Outcome Variable; Quantitative Features vs. Rental Counts; Let's Look at Categorical Features; Preparing the Data for Modeling; Regression Modeling; Simple Linear Regression; A Simple Model; Experimenting with Feature Engineering; Modeling with Polynomials

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