Using Deep Learning to Predict Basketball Scores

Deep Learning, TensorFlow, keras, neural networks, predictions, TensorFlow.JS

Evan Hennis

6 weeks · 4-6 hours per week

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.

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.
Peer support: Chat with other participants within the liveProject platform.
Compare with others: For each step, compare your deliverable to the solutions by the author and other participants.
Book and video resources: Excerpts from Manning books and videos are included, as well as references to other resources.

project outline

Introduction

Prerequisites Test

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

1.4 The Boston Housing Price Dataset

1.5 What’s TensorFlow?

1.6 What’s Keras?

1.7 Submit Your Work

Solution

2. Collecting and Cleaning Data

2.1 Collecting and Cleaning Data

2.2 Submit Your Work

Solution

3. Create a Neural Network

3.1 Create a Neural Network

3.2 Train/Test/Validate the Network

3.3 Submit Your Work

Solution

4. Common Training Errors

4.1 Common Training Errors

4.2 Building Your First Deep Neural Network: Introduction to Backpropagation

4.3 Submit Your Work

Solution

5. Hyper Parameter Tuning

5.1 Hyper Parameter Tuning

5.2 Submit Your Work

Solution

6. Deploying a Trained Network

6.1 Deploying a Trained Network

6.2 Create a Website to View Your Results

6.3 Submit Your Work

Solution

Summary

Project Conclusions

FAQs

Look inside

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