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06 Figure Code
Many of the figures used throughout this text are created in-place by code that appears in print. In
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05.15 Learning More
Further Machine Learning Resources This chapter has been a quick tour of machine learning in Python,
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05.14 Image Features
Application: A Face Detection Pipeline This chapter has explored a number of the central concepts an
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05.13 In Depth: Kernel Density Estimation
In the previous section we covered Gaussian mixture models (GMM), which are a kind of hybrid between
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05.12 In Depth: Gaussian Mixtures
The k-means clustering model explored in the previous section is simple and relatively easy to under
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05.11 In Depth: k-Means Clustering
In the previous few sections, we have explored one category of unsupervised machine learning models:
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05.10 In Depth: Manifold Learning
We have seen how principal component analysis (PCA) can be used in the dimensionality reduction task
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05.09 In Depth: Principal Component Analysis
Up until now, we have been looking in depth at supervised learning estimators: those estimators that
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05.08 In Depth: Decision Trees and Random Forests
Previously we have looked in depth at a simple generative classifier (naive Bayes; see In Depth: Nai
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05.07 In Depth: Support Vector Machines
Support vector machines (SVMs) are a particularly powerful and flexible class of supervised algorith
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05.06 In Depth: Linear Regression
Just as naive Bayes (discussed earlier in In Depth: Naive Bayes Classification) is a good starting p
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05.05 In Depth: Naive Bayes Classification
The previous four sections have given a general overview of the concepts of machine learning. In thi
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05.04 Feature Engineering
The previous sections outline the fundamental ideas of machine learning, but all of the examples ass
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05.03 Hyperparameters and Model Validation
In the previous section, we saw the basic recipe for applying a supervised machine learning model:
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05.02 Introducing Scikit Learn
There are several Python libraries which provide solid implementations of a range of machine learnin
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05.01 What Is Machine Learning
Before we take a look at the details of various machine learning methods, let's start by looking at
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05 Machine Learning
In many ways, machine learning is the primary means by which data science manifests itself to the br
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04.15 Further Resources
Matplotlib Resources A single chapter in a book can never hope to cover all the available features a
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04.14 Visualization With Seaborn
Matplotlib has proven to be an incredibly useful and popular visualization tool, but even avid users
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04.13 Geographic Data With Basemap
One common type of visualization in data science is that of geographic data. Matplotlib's main tool
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04.12 Three Dimensional Plotting
Matplotlib was initially designed with only two-dimensional plotting in mind. Around the time of the
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04.11 Settings and Stylesheets
Matplotlib's default plot settings are often the subject of complaint among its users. While much is
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04.10 Customizing Ticks
Matplotlib's default tick locators and formatters are designed to be generally sufficient in many co
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04.09 Text and Annotation
Creating a good visualization involves guiding the reader so that the figure tells a story. In some
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04.08 Multiple Subplots
Sometimes it is helpful to compare different views of data side by side. To this end, Matplotlib has
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04.07 Customizing Colorbars
Plot legends identify discrete labels of discrete points. For continuous labels based on the color o
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04.06 Customizing Legends
Plot legends give meaning to a visualization, assigning meaning to the various plot elements. We pre
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04.05 Histograms, Binnings, and Density
A simple histogram can be a great first step in understanding a dataset. Earlier, we saw a preview o
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04.04 Density and Contour Plots
Sometimes it is useful to display three-dimensional data in two dimensions using contours or color-c
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04.03 Errorbars
Visualizing Errors For any scientific measurement, accurate accounting for errors is nearly as impor
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04.02 Simple Scatter Plots
Another commonly used plot type is the simple scatter plot, a close cousin of the line plot. Instead
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04.01 Simple Line Plots
Perhaps the simplest of all plots is the visualization of a single function $y = f(x)$. Here we will
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04 Introduction To Matplotlib
Visualization with Matplotlib We'll now take an in-depth look at the Matplotlib package for visualiz
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03.13 Further Resources
In this chapter, we've covered many of the basics of using Pandas effectively for data analysis. Sti
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03.12 Performance Eval and Query
As we've already seen in previous sections, the power of the PyData stack is built upon the ability
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03.11 Working with Time Series
Pandas was developed in the context of financial modeling, so as you might expect, it contains a fai
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03.10 Working With Strings
One strength of Python is its relative ease in handling and manipulating string data. Pandas builds
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03.09 Pivot Tables
We have seen how the GroupBy abstraction lets us explore relationships within a dataset. A pivot tab
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03.08 Aggregation and Grouping
An essential piece of analysis of large data is efficient summarization: computing aggregations like
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03.07 Merge and Join
One essential feature offered by Pandas is its high-performance, in-memory join and merge operations
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