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Pythonで学ぶMachine Learningの前処理

中級スキルレベル

更新日 2025/12

機械学習のためのデータのクリーニングと準備方法について学びましょう！

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PythonMachine Learning

4時間

20 ビデオ

62 演習

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コース説明

このコースでは、データ前処理を「いつ」「どのように」行うかの基本を学びます。Machine Learning のあらゆるプロジェクトで不可欠なこの工程では、モデリングに向けてデータを整えます。データの取り込みとクリーニングから、Machine Learning モデルの学習までの間に前処理が入ります。データをモデルに適した形に標準化する方法、データセットの情報を最大限に活かす新しい特徴量の作成、モデルの適合を高めるための最適な特徴量の選択について学びます。最後に、UFO の目撃情報データセットを題材に、実際に前処理を行ってモデリングの準備をします。

前提条件

Cleaning Data in Python Supervised Learning with scikit-learn

1

Introduction to Data Preprocessing

In this chapter you'll learn exactly what it means to preprocess data. You'll take the first steps in any preprocessing journey, including exploring data types and dealing with missing data.

Introduction to preprocessing

Exploring missing data

Dropping missing data

Working with data types

Exploring data types

Converting a column type

Training and test sets

Class imbalance

Stratified sampling

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2

Standardizing Data

This chapter is all about standardizing data. Often a model will make some assumptions about the distribution or scale of your features. Standardization is a way to make your data fit these assumptions and improve the algorithm's performance.

Standardization

When to standardize

Modeling without normalizing

Log normalization

Checking the variance

Log normalization in Python

Scaling data for feature comparison

Scaling data - investigating columns

Scaling data - standardizing columns

Standardized data and modeling

KNN on non-scaled data

KNN on scaled data

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3

Feature Engineering

In this section you'll learn about feature engineering. You'll explore different ways to create new, more useful, features from the ones already in your dataset. You'll see how to encode, aggregate, and extract information from both numerical and textual features.

Feature engineering

Feature engineering knowledge test

Identifying areas for feature engineering

Encoding categorical variables

Encoding categorical variables - binary

Encoding categorical variables - one-hot

Engineering numerical features

Aggregating numerical features

Extracting datetime components

Engineering text features

Extracting string patterns

Vectorizing text

Text classification using tf/idf vectors

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4

Selecting Features for Modeling

This chapter goes over a few different techniques for selecting the most important features from your dataset. You'll learn how to drop redundant features, work with text vectors, and reduce the number of features in your dataset using principal component analysis (PCA).

Feature selection

When to use feature selection

Identifying areas for feature selection

Removing redundant features

Selecting relevant features

Checking for correlated features

Selecting features using text vectors

Exploring text vectors, part 1

Exploring text vectors, part 2

Training Naive Bayes with feature selection

Dimensionality reduction

Training a model with PCA

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5

Putting It All Together

Now that you've learned all about preprocessing you'll try these techniques out on a dataset that records information on UFO sightings.

UFOs and preprocessing

Checking column types

Dropping missing data

Categorical variables and standardization

Extracting numbers from strings

Identifying features for standardization

Engineering new features

Encoding categorical variables

Features from dates

Text vectorization

Feature selection and modeling

Selecting the ideal dataset

Modeling the UFO dataset, part 1

Modeling the UFO dataset, part 2

Congratulations!

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Pythonで学ぶMachine Learningの前処理

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