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.
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:
- Basics of Python
- Basics of pandas
- Basics of Google Colab
- Basics of TensorFlow and keras
- 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