Three-Project Series

Deep Learning for Basketball Scores Prediction you own this product

basics of Python, TensorFlow, Keras, and deep learning
skills learned
use pandas to standardize data • create a neural network with Keras • convert trained deep learning networks for use on the web
Evan Hennis
3 weeks · 4-6 hours per week average · INTERMEDIATE

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5, 10 or 20 seats+ for your team - learn more

In this liveProject series, you’ll step into the role of a data scientist trying to predict the results of NCAA college basketball games. Your client’s favorite team is competing, and he wants to know how close their games will be. Each liveProject in this series covers a different aspect of the machine learning pipeline from creating the initial model to deploying it to the web and Android for your client’s easy use.

These projects are designed for learning purposes and are not complete, production-ready applications or solutions.

here's what's included

Project 1 Create a Neural Network
In this liveProject, you’ll use Keras to create a deep learning model for predicting basketball scores. Once your model is created, you’ll train it up on sample data and then validate your results to ensure it’s still accurate when applied to data from the real world.
Project 2 Deploy a Predictor on the Web
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.
Project 3 Deploy a Predictor on Android
In this liveProject, you’ll create an Android application that can run a pretrained basketball predictor deep learning model for the easy use of your client. Your challenges will include converting the DataFrames into JavaScript arrays, converting your model into a TensorFlow Lite model, and finally packaging the model inside a working Android application.

book resources

When you start each of the projects in this series, you'll get full access to the following book for 90 days.

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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.


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

  • Basics of Python
  • Basics of pandas
  • Basics of Google Colab
  • Basics of TensorFlow and Keras
  • Basics of deep learning

you will learn

In this liveProject series, you’ll learn all the steps needed to deliver a working machine learning project to a client.

  • Using pandas to manipulate data to build training and testing data frames
  • Setting up a deep learning network
  • Creating a Keras neural network
  • Testing and validating a neural network


You choose the schedule and decide how much time to invest as you build your project.
Project roadmap
Each project is divided into several achievable steps.
Get Help
While within the liveProject platform, get help from other participants and our expert mentors.
Compare with others
For each step, compare your deliverable to the solutions by the author and other participants.
book resources
Get full access to select books for 90 days. Permanent access to excerpts from Manning products are also included, as well as references to other resources.