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

filed under

Data Science

This title has been retired and is no longer for sale.

Look inside

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.