Our First Machine Learning Project
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What we will learn here
Let's go through a simple ML application & create our first project. Parallely, we will learn some new concepts & terms.
We will use Iris Dataset that contains 4 features/ measurements (petal length, petal width, sepal length, sepal width) of 50 samples of 3 species of Iris flower
(Iris setosa, Iris Versicolor, Iris Virginica) = 50×3 = 150 samples in total. This is one of the best known datasets till date.
For more details on the dataset, visit UCI Machine Learning Repository - IRIS dataset.
For reference, here are pictures of the three flowers species:
Here is picture showing the 4 measurements made from each flower:
Based on these 4 measurements, we can be certain of which species each iris flower belongs to. For a while, we will become hobby botanists trying to classify the flowers into their own species.
Our goal is to build a ML model that learns from the 4 mesurements/ features of the iris flowers whose species is known.
Then, using the learned model, we will predict the species of a new iris flower.
This type of problem is called as Supervised Learning Multi-Class Classification problem.
Supervised Learning- We know the correct species (target class) for each iris flower (3 species).Classification- We want to predict one of the several classes (the species of iris).Multi Class- There are 3 types of species we are dealing with, hence the name multi-class.Classes- Possible outputs (different species of irises)label- For a particular flower, the species it belongs to it is called label.
First ML model - Notebook
To download the github pages from command line,
- For Windows users,
- Download Curl.
- Copy the
curl.exefrom the bin folder and Paste it inC>Program files& run it once. - To download a github file, open the cmd window & type
curl -LJO https://GitHub-URL-to-download/
- For Linux users
- To download a github file,
wget --no-check-certificate --content-disposition https://GitHub-URL-to-download/