Three-Project Series
Deep Learning for Basketball Scores Prediction you own this product
- prerequisites
- 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
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.
choose your plan
pro
monthly
annual
$24.99
$249.99
only $20.83 per month
- share your subscription with one other person
- access to all Manning books, MEAPs, liveVideos, liveProjects, and audiobooks!
- choose another free eBook every time you renew
- choose twelve free eBooks per year
- exclusive 50% discount on all purchases
-
![]()
Deep Learning for Basketball Scores Prediction eBook for free
Deep Learning for Basketball Scores Prediction eBook for free
team
monthly
annual
$49.99
$499.99
only $41.67 per month
- five seats for your team
- access to all Manning books, MEAPs, liveVideos, liveProjects, and audiobooks!
- choose another free eBook every time you renew
- choose twelve free eBooks per year
- exclusive 50% discount on all purchases
-
![]()
Deep Learning for Basketball Scores Prediction eBook for free
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 the following:
TOOLS
- 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
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.
