IPython

Many of the figures used throughout this text are created in-place by code that appears in print. In

Further Machine Learning Resources This chapter has been a quick tour of machine learning in Python,

Application: A Face Detection Pipeline This chapter has explored a number of the central concepts an

In the previous section we covered Gaussian mixture models (GMM), which are a kind of hybrid between

The k-means clustering model explored in the previous section is simple and relatively easy to under

In the previous few sections, we have explored one category of unsupervised machine learning models:

We have seen how principal component analysis (PCA) can be used in the dimensionality reduction task

Up until now, we have been looking in depth at supervised learning estimators: those estimators that

Previously we have looked in depth at a simple generative classifier (naive Bayes; see In Depth: Nai

Support vector machines (SVMs) are a particularly powerful and flexible class of supervised algorith

Just as naive Bayes (discussed earlier in In Depth: Naive Bayes Classification) is a good starting p

The previous four sections have given a general overview of the concepts of machine learning. In thi

The previous sections outline the fundamental ideas of machine learning, but all of the examples ass

In the previous section, we saw the basic recipe for applying a supervised machine learning model:

There are several Python libraries which provide solid implementations of a range of machine learnin

Before we take a look at the details of various machine learning methods, let's start by looking at

In many ways, machine learning is the primary means by which data science manifests itself to the br

Matplotlib Resources A single chapter in a book can never hope to cover all the available features a

Matplotlib has proven to be an incredibly useful and popular visualization tool, but even avid users

One common type of visualization in data science is that of geographic data. Matplotlib's main tool

Matplotlib was initially designed with only two-dimensional plotting in mind. Around the time of the

Matplotlib's default plot settings are often the subject of complaint among its users. While much is

Matplotlib's default tick locators and formatters are designed to be generally sufficient in many co

Creating a good visualization involves guiding the reader so that the figure tells a story. In some

Sometimes it is helpful to compare different views of data side by side. To this end, Matplotlib has

Plot legends identify discrete labels of discrete points. For continuous labels based on the color o

Plot legends give meaning to a visualization, assigning meaning to the various plot elements. We pre

A simple histogram can be a great first step in understanding a dataset. Earlier, we saw a preview o

Sometimes it is useful to display three-dimensional data in two dimensions using contours or color-c

Visualizing Errors For any scientific measurement, accurate accounting for errors is nearly as impor

Another commonly used plot type is the simple scatter plot, a close cousin of the line plot. Instead

Perhaps the simplest of all plots is the visualization of a single function $y = f(x)$. Here we will

Visualization with Matplotlib We'll now take an in-depth look at the Matplotlib package for visualiz

In this chapter, we've covered many of the basics of using Pandas effectively for data analysis. Sti

As we've already seen in previous sections, the power of the PyData stack is built upon the ability

Pandas was developed in the context of financial modeling, so as you might expect, it contains a fai

One strength of Python is its relative ease in handling and manipulating string data. Pandas builds

We have seen how the GroupBy abstraction lets us explore relationships within a dataset. A pivot tab

An essential piece of analysis of large data is efficient summarization: computing aggregations like

One essential feature offered by Pandas is its high-performance, in-memory join and merge operations