From the course: Deep Learning: Getting Started

The Iris classification problem

From the course: Deep Learning: Getting Started

The Iris classification problem

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- [Instructor] Having learned the basics of deep learning with neural networks, let's apply the learnings in a simple example. In this chapter, we will build a deep learning model to predict the type of flower with the Iris dataset. The Iris dataset is a popular machine learning dataset, and the students may already be familiar with this. The dataset has four feature variables about the flower, namely, sepal length, sepal width, petal length, and petal width. The target class is the type of species. There are three classes here, namely, setosa, versicolor, and virginica. The goal for this chapter is to build a deep learning model with Keras 2.0 to predict the type of species. The code for this chapter is available in the notebook, code_04_XX Iris - Deep Learning Classification Example. This is available as part of the exercise files we installed earlier. Let's open the notebook now. The setup code block contains a list of dependencies that are needed for the exercises. Run the setup block once to install all the dependencies. Depending on whether they are already available in your setup or not, this may take some time to run. You only need to run it once to install the dependencies. The dependencies are installed now.

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