Deploy a Predictor on the Web

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prerequisites
basics of Python, TensorFlow, Keras, and JavaScript
skills learned
converting a model to web use • generating JavaScript usable data sets • simple web development
Evan Hennis
1 week · 4-6 hours per week · INTERMEDIATE
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catalog / Data Science / Deep Learning
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In this liveProject, you’ll deploy a pretrained basketball predictor deep learning model onto the web for easy use by clients. You’ll utilize the powerful TensorFlow.js framework to ensure the model works in the browser, as well as converting DataFrames into JavaScript arrays, and building a simple website around the model and data sets.
This project is designed for learning purposes and is not a complete, production-ready application or solution.

project author

Evan Hennis
Evan Hennis is a Google Developer Expert in Machine Learning and an international speaker. He has an undergraduate degree in Computer Science from Iowa State University and a Master's degree in Computer Science with a specialization in machine learning from Georgia Tech. He has spent over sixteen years in software development, working across multiple languages and domains.

prerequisites

This project is for intermediate Python programmers looking to enhance their data science skills with useful techniques for deploying deep learning. To begin this liveProject, you will need to be familiar with:

TOOLS
  • Basics of Python
  • Basics of pandas
  • Basics of Google Colab
  • Basics of TensorFlow and Keras
  • Basics of HTML and JavaScript
TECHNIQUES
  • Basics of machine learning
  • Basics of web app development

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