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 and the capabilities of bringing ML models to production. 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 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
Create a mobile application with Java and Android Studio