Data Analysis From Scratch With Python: Step By Step Guide

9. Supervised & Unsupervised Learning
In many introductory courses and books about machine learning and data
science, you’ll likely encounter what Supervised & Unsupervised Learning mean
and what are their differences. That’s because these are the two general
categories of machine learning and data science tasks many professionals do.
What is Supervised Learning?
First, Supervised Learning is a lot similar to learning from examples. For
instance, we have a huge collection of images correctly labeled as either dogs or
cats. Our computer will then learn from those given examples and correct labels.
Perhaps our computer will find patterns and similarities among those images.
And finally when we introduce new images, our computer and model will
successfully identify an image whether there’s a dog or cat in it.
It’s a lot like learning with supervision. There are correct answers (e.g. cats or
dogs) and it’s the job of our model to align itself so on new data it can still
produce correct answers (in an acceptable performance level because it’s hard to
reach 100%).
For example, Linear Regression is considered under Supervised Learning.
Remember that in linear regression we’re trying to predict the value of y for a
given x. But first, we have to find patterns and “fit” a line that best describes the
relationship between x and y (and predict y values for new x inputs).
print(__doc__)

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# Code source: Jaques Grobler
# License: BSD 3 clause
import matplotlib.pyplot as plt
import numpy as np
from sklearn import datasets, linear_model
from sklearn.metrics import mean_squared_error, r2_score
# Load the diabetes dataset
diabetes = datasets.load_diabetes()
# Use only one feature