Load a TensorFlow Dataset with Web Data

Scenario

You recently discovered some flowers while on a hike that you're pretty sure are irises. You'd love to know what they are so you can plant some yourself, so you took pictures and measurements. Luckily, you found a set of data describing iris flowers that you can use to train a TensorFlow model to predict the iris species. Download the data, parse it, and load it into a TensorFlow Dataset for easy consumption by your model.

Iris data, hosted by the UC Irvine Machine Learning Repository.

Lab Goals

1. Retrieve Iris data from the internet.
2. Parse the Iris data.
3. Load the Iris data into a TensorFlow Dataset.

Logging In To the Lab Environment

No environment is provided for this lab. This lab is meant to be completed in PyCharm running on your own hardware in preparation for the TensorFlow Developer Certificate Exam. If you don't have PyCharm or are not working toward the certification, you can use the provided Google account credentials, or your own account, to complete the tasks in Google Colab. If you use the provided credentials to access Colab, make sure to save a copy of your work locally before the end of the lab.