本講義資料について 本ページは 日本メディカルAI学会公認資格：メディカルAI専門コースのオンライン講義資料（以下本資料） です． 本講料を読むことで，医療で人工知能技術を使う際に最低限必要な知識や実

健康意識の高まりや運動人口の増加に伴って，活動量計などのウェアラブルデバイスが普及し始めています．センサーデバイスから心拍数などの情報を取得することで，リアルタイムに健康状態をモニタリングできる可能性

近年，次世代シーケンサ（NGS; Next Generation Sequencer）の発展により，遺伝子の塩基配列が高速，大量，安価に読み取られるようになってきました． ここではディープラーニングを

ここでは血液細胞の検出タスクに取り組みます．人の血液の顕微鏡画像が与えられたときに， 赤血球（Red Blood Cell; RBC） 白血球（White Blood Cell; WBC） 血小板（

画像を対象とした深層学習の応用技術には様々なものがあります．例えば，画像の中の個別の物体の周りを矩形で囲むようにして検出する物体検出や，画像内で個別物体が占める領域を認識する画像セグメンテーションなど

ChainerはDeep Learningフレームワークの一つで，現在様々なDeep Learningフレームワーク（TensorFlow, PyTorch, etc.）でも採用され主要なニューラルネ

ここでは，ニューラルネットワーク (Neural Network) についてその概要を紹介していきます．画像認識などに用いられる Convolutional Neural Network (CNN)

本章では，基礎的な機械学習手法として代表的な単回帰分析と重回帰分析の仕組みを、数式を用いて説明します． ここで単回帰分析と重回帰分析を紹介することには 2 つの理由があります． 1 つ目は，回帰分析と

本章では，ディープラーニングを含めた機械学習に必要な数学の基礎である「微分」「線形代数」「確率・統計」の3つについて，簡潔に紹介していきます． 機械学習とは 機械学習は，コンピュータがデータから学習す

私はもともとこれをソフトウェアエンジニアになるための短いトピックリストとして作成しましたが、 今日それは大きなリストに成長しました。この調査計画を経て、私はAmazonで ソフトウェアエンジニアとし

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

Some of the most interesting studies of data come from combining different data sources. These opera

Up to this point we've been focused primarily on one-dimensional and two-dimensional data, stored in

The difference between data found in many tutorials and data in the real world is that real-world da

One of the essential pieces of NumPy is the ability to perform quick element-wise operations, both w

In Chapter 2, we looked in detail at methods and tools to access, set, and modify values in NumPy ar

At the very basic level, Pandas objects can be thought of as enhanced versions of NumPy structured a

In the previous chapter, we dove into detail on NumPy and its ndarray object, which provides efficie

I originally created this as a short to-do list of study topics for becoming a software engineer, b

While often our data can be well represented by a homogeneous array of values, sometimes this is not

Up to this point we have been concerned mainly with tools to access and operate on array data with N

In the previous sections, we saw how to access and modify portions of arrays using simple indices (e

This section covers the use of Boolean masks to examine and manipulate values within NumPy arrays. M

We saw in the previous section how NumPy's universal functions can be used to vectorize operations a

Often when faced with a large amount of data, a first step is to compute summary statistics for the

Up until now, we have been discussing some of the basic nuts and bolts of NumPy; in the next few sec

Data manipulation in Python is nearly synonymous with NumPy array manipulation: even newer tools lik

Effective data-driven science and computation requires understanding how data is stored and manipula

This chapter, along with chapter 3, outlines techniques for effectively loading, storing, and manipu

In this chapter, we've just scratched the surface of using IPython to enable data science tasks. Muc

In the process of developing code and creating data processing pipelines, there are often trade-offs

Code development and data analysis always require a bit of trial and error, and IPython contains too

When working interactively with the standard Python interpreter, one of the frustrations is the need

Previously we saw that the IPython shell allows you to access previous commands with the up and down

The previous two sections showed how IPython lets you use and explore Python efficiently and interac

If you spend any amount of time on the computer, you've probably found a use for keyboard shortcuts

If you read no other section in this chapter, read this one: I find the tools discussed here to be t

There are many options for development environments for Python, and I'm often asked which one I use

BOOK_INFORMATION This notebook contains an excerpt from the Python Data Science Handbook by Jake Va

Numpyは行列計算などの数値計算をを効率的に行うことができるライブラリです。機械学習では必須となる知識なので，このnotebookで基本的な使い方を学びましょう。 ライブラリをインポートします。nu