Using Deep Learning to Predict Basketball Scores

prerequisites
Intermediate Python, Beginner TensorFlow, Basics of Deep Learning
skills learned
Use Pandas to standardize data, Create Neural Network with Keras, Convert trained Deep Learning network for use on the web, Convert trained Deep Learning network to h5 for use with TensorFlow.js
Evan Hennis
6 weeks · 4-6 hours per week · INTERMEDIATE
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In this liveProject, 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 playing tonight, and he wants to know how close the game will be. Your challenge is to run a large dataset of previous game results through a neural network, and assess its predictions. To do this, you’ll have to prepare and clean your data, create and train a Keras neural network, optimize its output, and then deploy it to the web for your client’s easy use.
This project is designed for learning purposes and is not a complete, production-ready application or solution.

book resources

When you start your liveProject, you get full access to the following books for 90 days.

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 deep learning techniques. 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

TECHNIQUES
  • Basics of machine learning

you will learn

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

  • Setting up your data science environment
  • Installing TensorFlow, Pandas, and Matplotlib
  • Collecting and cleaning your data
  • Using Pandas to standardize data
  • Setting up a deep learning network
  • Creating a Keras neural network
  • Converting your trained deep learning network for use on the web
  • Converting the trained network to h5 for use with TensorFlow.JS

features

Self-paced
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.

project outline

Introduction

Prerequisites Test

New module

Get Started

1. Setting up the Google Colab Notebook

1.1 Setting up the Google Colab Notebook

1.2 Manipulating Data with pandas

1.3 Create a Neural Network that can Convert Celsius to Fahrenheit

The Boston Housing Price Dataset

What’s TensorFlow?

What’s Keras?

1.4 Submit Your Work

2. Collecting and Cleaning Data

2.1 Collecting and Cleaning Data

Loading Data into DataFrames

Cleaning and Transforming DataFrames

2.2 Submit Your Work

3. Create a Neural Network

3.1 Create a Neural Network

3.2 Train/Test/Validate the Network

Getting Started with Neural Networks

3.3 Submit Your Work

4. Common Training Errors

4.1 Common Training Errors

Building Your First Deep Neural Network: Introduction to Backpropagation

Overfitting and Underfitting

4.2 Submit Your Work

5. Hyper Parameter Tuning

5.1 Hyper Parameter Tuning

Regularizing Your Model and Tuning Your Hyperparameters

5.2 Submit Your Work

6. Deploying a Trained Network

6.1 Deploying a Trained Network

6.2 Create a Website to View Your Results

Transfer Learning: Reusing Pretrained Neural Networks

6.3 Submit Your Work

Summary

Project Conclusions

FAQs

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