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

# <!--BOOK_INFORMATION-->
# <img align="left" style="padding-right:10px;" src="figures/PDSH-cover-small.png">
# *This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*
# 
# *The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!*

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# < [Further Resources](04.15-Further-Resources.ipynb) | [Contents](Index.ipynb) | [What Is Machine Learning?](05.01-What-Is-Machine-Learning.ipynb) >

# # Machine Learning

# In many ways, machine learning is the primary means by which data science manifests itself to the broader world.
# Machine learning is where these computational and algorithmic skills of data science meet the statistical thinking of data science, and the result is a collection of approaches to inference and data exploration that are not about effective theory so much as effective computation.
# 
# The term "machine learning" is sometimes thrown around as if it is some kind of magic pill: *apply machine learning to your data, and all your problems will be solved!*
# As you might expect, the reality is rarely this simple.
# While these methods can be incredibly powerful, to be effective they must be approached with a firm grasp of the strengths and weaknesses of each method, as well as a grasp of general concepts such as bias and variance, overfitting and underfitting, and more.
# 
# This chapter will dive into practical aspects of machine learning, primarily using Python's [Scikit-Learn](http://scikit-learn.org) package.
# 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 of this chapter 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 most important 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!
# 
# Finally, if you are seeking a more comprehensive or technical treatment of any of these subjects, I've listed several resources and references in [Further Machine Learning Resources](05.15-Learning-More.ipynb).

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# < [Further Resources](04.15-Further-Resources.ipynb) | [Contents](Index.ipynb) | [What Is Machine Learning?](05.01-What-Is-Machine-Learning.ipynb) >