Today, I will learn scikit-learn by the notes of scipy-lecture. (You can see the scipy-lecture notes on www.scipy-lectures.org/ and there was a PDF of Chinese Sample version in the internet)
1. Introduction
Machine learning is about building programs with tunable parameters that are adjusted automatically so as to improve their behavior by adapting to previously seen data.
Machine learning can be considered as a subfield of Artificial Intelligence since those algorithms can be seen as building blocks to make computers learn to behave more intelligently by somehow generalizing rather that just storing and retrieving data items like a database system would do.
1.1 Two machine learning problems: Classification and Regression
1.1.1 Classification
1.1.2 Regression
1.2 Data in scikit-learn
Machine learning algorithms implemented in scikit-learn expect data to be stored in a two-dimensional array or matrix.
The size of the array is expected to be [n_samples, n_features]
iris data was introduced in previous study.
(two ways of import data shown as follows)
from sklearn.datasets import load_iris
iris = load_iris()
from sklearn import datasets
iris = datasets.load_iris()
