A basic program to recognize Iris flowers.

Using a well known dataset that contains 150 samples of Iris Flowers. This specific data set comes pre loaded in scikit learner.

Each Sample has a label - Iris Setosa , Iris Veriscolor, and Iris Virginica. Each Sample has it's own set of features - Petal length/Width and Sepal Length/Width.

Type of Learning - [Supervised Learning] If we didn't have labels it would be called Unsupervised Learning but since it has labels it would be catagorized as Supervised learning.

Given the numerous amount of Machine learning algorithms, How do you know which one to utilize? Our goal is to catagorize a flower , whether it be an Iris flower or NOT an Iris flower.

This would fall under a classification model therefore we need to use a classifer! But..what type?

It depends on the size of your data! Currently , If you have a lot of data , deep neural networks outperform many generic ML algorithms. In our case , we only have 150 samples so we'll use a Linear Classifier.

We'll set the clas parameter to 3 and move on to training the model!

Since we're using a classifer , We just need to call the FIT method on our object to trian our model. FIT is a training algorithm that allows us to input our training data into a model , finding patterns in the data.

Now when we input a new flower , it will be able to determine whether or not it's an Iris flower!

Special thanks to Cornell Univeristy for the free information provided on their website : http://machinelearning.cis.cornell.edu/