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
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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.
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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