#!/usr/bin/env python
# coding: utf-8

# # Machine Learning

# This final part is an introduction to the very broad topic of machine learning, mainly via Python's [Scikit-Learn](http://scikit-learn.org) package.
# You can think of machine learning as a class of algorithms that allow a program to detect particular patterns in a dataset, and thus "learn" from the data to draw inferences from it.
# This is not meant to be a comprehensive introduction to the field of machine learning; that is a large subject and necessitates a more technical approach than we take here.
# Nor is it meant to be a comprehensive manual for the use of the Scikit-Learn package (for this, you can refer to the resources listed in [Further Machine Learning Resources](05.15-Learning-More.ipynb)).
# Rather, the goals here are:
# 
# - To introduce the fundamental vocabulary and concepts of machine learning
# - To introduce the Scikit-Learn API and show some examples of its use
# - To take a deeper dive into the details of several of the more important classical machine learning approaches, and develop an intuition into how they work and when and where they are applicable
# 
# Much of this material is drawn from the Scikit-Learn tutorials and workshops I have given on several occasions at PyCon, SciPy, PyData, and other conferences.
# Any clarity in the following pages is likely due to the many workshop participants and co-instructors who have given me valuable feedback on this material over the years!