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
Project 3 Deploy a Predictor on Android
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:
- Basics of Python
- Basics of pandas
- Basics of Google Colab
- Basics of TensorFlow and Keras
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