Three-Project Series

Deep Learning for Basketball Scores Prediction

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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.
$29.99 $19.99
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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.
$29.99 $19.99
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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.
$29.99 $19.99
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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 and the capabilities of bringing ML models to production. 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 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, and deploy it to the platform of your choice. 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 Converting a model for use on the web with Python and TensorFlow Converting a trained network to H5 to use with TensorFlow Lite Generate usable data sets accessible in JavaScript using pandas and NumPy Build a simple website with HTML and JavaScript Create a mobile application with Java and Android Studio

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.
includes 3 liveProjects
liveProject $49.99 $59.99 self-paced learning
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