rdf-syntax-grammar
mods:
  schema location: http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
  title info:
    title: Monetizing Machine Learning
    sub title: Quickly Turn Python ML Ideas into Web Applications on the Serverless Cloud
  name:
    name part: Amunategui, Manuel.
    role:
      role term: creator
    role:
      role term: author.
    role:
      role term: aut
    role:
      role term: http://id.loc.gov/vocabulary/relators/aut
  name:
    name part: Roopaei, Mehdi.
    role:
      role term: author.
    role:
      role term: aut
    role:
      role term: http://id.loc.gov/vocabulary/relators/aut
  name:
    name part: SpringerLink (Online service)
  type of resource: text
  origin info:
    place:
      place term: xxu
    date issued: 2018
    edition: 1st ed. 2018.
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: electronic
    form: electronic resource
    reformatting quality: access
    form: remote
    extent: XLI, 482 p. 319 illus. online resource.
  abstract: Take your Python machine learning ideas and create serverless web applications accessible by anyone with an Internet connection. Some of the most popular serverless cloud providers are covered in this book-Amazon, Microsoft, Google, and PythonAnywhere. You will work through a series of common Python data science problems in an increasing order of complexity. The practical projects presented in this book are simple, clear, and can be used as templates to jump-start many other types of projects. You will learn to create a web application around numerical or categorical predictions, understand the analysis of text, create powerful and interactive presentations, serve restricted access to data, and leverage web plugins to accept credit card payments and donations. You will get your projects into the hands of the world in no time. Each chapter follows three steps: modeling the right way, designing and developing a local web application, and deploying onto a popular and reliable serverless cloud provider. You can easily jump to or skip particular topics in the book. You also will have access to Jupyter notebooks and code repositories for complete versions of the code covered in the book. What You'll Learn: Extend your machine learning models using simple techniques to create compelling and interactive web dashboards Leverage the Flask web framework for rapid prototyping of your Python models and ideas Create dynamic content powered by regression coefficients, logistic regressions, gradient boosting machines, Bayesian classifications, and more Harness the power of TensorFlow by exporting saved models into web applications Create rich web dashboards to handle complex real-time user input with JavaScript and Ajax to yield interactive and tailored content Create dashboards with paywalls to offer subscription-based access Access API data such as Google Maps, OpenWeather, etc. Apply different approaches to make sense of text data and return customized intelligence Build an intu…
  table of contents: Chapter 1 Introduction to Serverless Technologies -- Chapter 2 Client-Side Intelligence using Regression Coefficients on Azure -- Chapter 3 Real-Time Intelligence with Logistic Regression on GCP -- Chapter 4 Pre-Trained Intelligence with Gradient Boosting Machine on AWS -- Chapter 5 Case Study Part 1: Supporting Both Web and Mobile Browsers -- Chapter 6 Displaying Predictions with Google Maps on Azure -- Chapter 7 Forecasting with Naive Bayes and OpenWeather on AWS -- Chapter 8 Interactive Drawing Canvas and Digit Predictions using TensorFlow on GCP -- Chapter 9 Case Study Part 2: Displaying Dynamic Charts -- Chapter 10 Recommending with Singular Value Decomposition on GCP -- Chapter 11 Simplifying Complex Concepts with NLP and Visualization on Azure -- Chapter 12 Case Study Part 3: Enriching Content with Fundamental Financial Information -- Chapter 13 Google Analytics -- Chapter 14 A/B Testing on PythonAnywhere and MySQL -- Chapter 15 From Visitor To Subscriber -- Chapter 16 Case Study Part 4: Building a Subscription Paywall with Memberful -- Chapter 17 Conclusion.-.
  note: by Manuel Amunategui, Mehdi Roopaei.
  subject:
    topic: Artificial intelligence
  subject:
    topic: Computer communication systems
  subject:
    topic: Big data
  subject:
    topic: Artificial Intelligence
  subject:
    topic: Computer Communication Networks
  subject:
    topic: Big Data
  classification: Q334-342
  classification: 006.3
  related item:
    title info:
      title: Springer eBooks
  identifier: 9781484238738
  identifier: https://doi.org/10.1007/978-1-4842-3873-8
  record info:
    record creation date: 180912
    record change date: 20191029012433.0
    record identifier: 978-1-4842-3873-8