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kabukiage
【保存版】公開ダッシュボードまとめ
一般企業や官公庁など、ダッシュボード作成をするときに私が参考にしているサイトをまとめました。
2か月前
・4 min read
Data science
ダッシュボード
data analysis
char
【Rで図解】大数の法則と中心極限定理の理解が曖昧になりやすいのはなぜか
# 目次 - はじめに - 大数の法則とは - 中心極限定理とは - 終わりに - 参考文献・サイト# はじめに **※2022/5/17：一部解説に誤りがありましたので内容修正いたしました。大変申し
21か月前
・4 min read
数学
Data science
R
snitch
R4.2.0をWSL2のUbuntu20.04にインストールする
Magicodeでは初投稿となります。いろいろな機能を試させていただきます。 ## R4.2.0の主な変更点 `|>`(native pipe)というパイプが実用レベルになったことで、これまでのデ
21か月前
・4 min read
Data science
R
Ubuntu
Best Free Materials 4U
06 Figure Code
Many of the figures used throughout this text are created in-place by code that appears in print. In
29か月前
・56 min read
python
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05.15 Learning More
Further Machine Learning Resources This chapter has been a quick tour of machine learning in Python,
29か月前
・5 min read
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05.14 Image Features
Application: A Face Detection Pipeline This chapter has explored a number of the central concepts an
29か月前
・18 min read
python
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Numpy
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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
29か月前
・29 min read
python
Matplotlib
Numpy
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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
29か月前
・21 min read
python
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Numpy
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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:
29か月前
・22 min read
python
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Numpy
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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
29か月前
・27 min read
python
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Numpy
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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
29か月前
・24 min read
python
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Numpy
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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
29か月前
・17 min read
python
Matplotlib
Numpy
Best Free Materials 4U
05.07 In Depth: Support Vector Machines
Support vector machines (SVMs) are a particularly powerful and flexible class of supervised algorith
29か月前
・26 min read
python
Matplotlib
Numpy
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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
29か月前
・26 min read
python
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Numpy
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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
29か月前
・16 min read
python
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Numpy
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05.04 Feature Engineering
The previous sections outline the fundamental ideas of machine learning, but all of the examples ass
29か月前
・16 min read
python
Matplotlib
Numpy
Best Free Materials 4U
05.03 Hyperparameters and Model Validation
In the previous section, we saw the basic recipe for applying a supervised machine learning model:
29か月前
・31 min read
python
Matplotlib
Numpy
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05.02 Introducing Scikit Learn
There are several Python libraries which provide solid implementations of a range of machine learnin
29か月前
・31 min read
python
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Numpy
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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
29か月前
・19 min read
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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
29か月前
・3 min read
python
Matplotlib
Numpy
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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
29か月前
・4 min read
python
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Numpy
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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
29か月前
・20 min read
python
Matplotlib
Numpy
Best Free Materials 4U
04.13 Geographic Data With Basemap
One common type of visualization in data science is that of geographic data. Matplotlib's main tool
29か月前
・22 min read
python
Matplotlib
Numpy
Best Free Materials 4U
04.12 Three Dimensional Plotting
Matplotlib was initially designed with only two-dimensional plotting in mind. Around the time of the
29か月前
・12 min read
python
Matplotlib
Numpy
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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
29か月前
・10 min read
python
Matplotlib
Numpy
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04.10 Customizing Ticks
Matplotlib's default tick locators and formatters are designed to be generally sufficient in many co
29か月前
・11 min read
python
Matplotlib
Numpy
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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
29か月前
・14 min read
python
Matplotlib
Numpy
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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
29か月前
・9 min read
python
Matplotlib
Numpy
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04.07 Customizing Colorbars
Plot legends identify discrete labels of discrete points. For continuous labels based on the color o
29か月前
・12 min read
python
Matplotlib
Numpy
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04.06 Customizing Legends
Plot legends give meaning to a visualization, assigning meaning to the various plot elements. We pre
29か月前
・8 min read
python
Matplotlib
Numpy
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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
29か月前
・7 min read
python
Matplotlib
Numpy
Best Free Materials 4U
04.04 Density and Contour Plots
Sometimes it is useful to display three-dimensional data in two dimensions using contours or color-c
29か月前
・7 min read
python
Matplotlib
Numpy
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04.03 Errorbars
Visualizing Errors For any scientific measurement, accurate accounting for errors is nearly as impor
29か月前
・7 min read
python
Matplotlib
Numpy
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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
29か月前
・7 min read
python
Matplotlib
Numpy
Best Free Materials 4U
04.01 Simple Line Plots
Perhaps the simplest of all plots is the visualization of a single function $y = f(x)$. Here we will
29か月前
・10 min read
python
Matplotlib
Numpy
Best Free Materials 4U
04 Introduction To Matplotlib
Visualization with Matplotlib We'll now take an in-depth look at the Matplotlib package for visualiz
29か月前
・15 min read
python
Matplotlib
Numpy
Best Free Materials 4U
03.13 Further Resources
In this chapter, we've covered many of the basics of using Pandas effectively for data analysis. Sti
29か月前
・3 min read
python
Matplotlib
Numpy
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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
29か月前
・14 min read
python
Matplotlib
Numpy
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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
29か月前
・38 min read
python
Matplotlib
Numpy
Best Free Materials 4U
03.10 Working With Strings
One strength of Python is its relative ease in handling and manipulating string data. Pandas builds
29か月前
・23 min read
python
Matplotlib
Numpy
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