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

# Machine Learning is a huge and growing area. In this chapter, we cannot
# possibly even survey this area, but we can provide some context and some
# connections to probability and statistics that should make it easier to think
# about machine learning and how to apply these methods to real-world problems.
# The fundamental problem of statistics is basically the same as machine
# learning: given some data, how to make it actionable?  For statistics, the
# answer is to construct analytic estimators using powerful theory. For machine
# learning, the answer is algorithmic prediction. Given a data set, what
# forward-looking inferences can we draw? There is a subtle bit in this
# description: how can we know the future if all we have is data about the past?
# This is the crux of the matter for machine learning, as we will explore in the
# chapter.