课程

使用 scikit-learn 的监督学习

中级技能水平

更新时间 2025年12月

用 Python 中的 scikit-learn 提升你的机器学习技能。在这门互动课程中使用真实世界数据集，学习如何做出强大的预测！

免费开始课程

PythonMachine Learning

4小时

15 视频

49 道练习

4,050 XP

280K+

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课程描述

通过 scikit-learn 提升您的机器学习技能，学习如何使用这一流行的 Python 库在有标签数据上训练模型。在本课程中，您将学习如何进行有效预测，例如客户是否会流失、个人是否患有糖尿病，甚至是对歌曲的流派进行分类。借助真实世界的数据集，您将了解如何构建预测模型、调优其参数，并评估它们在未见过数据上的表现。视频包含实时字幕，您可以点击视频左下角的 "Show transcript" 展开查看。课程术语表位于右侧的资源部分。若要获得 CPE 学分，您需要完成课程并在合格测评中达到 70% 的得分。您可以点击右侧的 CPE 学分提示跳转至测评页面。

先决条件

Introduction to Statistics in Python

1

Classification

In this chapter, you'll be introduced to classification problems and learn how to solve them using supervised learning techniques. You'll learn how to split data into training and test sets, fit a model, make predictions, and evaluate accuracy. You’ll discover the relationship between model complexity and performance, applying what you learn to a churn dataset, where you will classify the churn status of a telecom company's customers.

Machine learning with scikit-learn

Binary classification

The supervised learning workflow

The classification challenge

k-Nearest Neighbors: Fit

k-Nearest Neighbors: Predict

Measuring model performance

Train/test split + computing accuracy

Overfitting and underfitting

Visualizing model complexity

2

Regression

In this chapter, you will be introduced to regression, and build models to predict sales values using a dataset on advertising expenditure. You will learn about the mechanics of linear regression and common performance metrics such as R-squared and root mean squared error. You will perform k-fold cross-validation, and apply regularization to regression models to reduce the risk of overfitting.

Introduction to regression

Creating features

Building a linear regression model

Visualizing a linear regression model

The basics of linear regression

Fit and predict for regression

Regression performance

Cross-validation

Cross-validation for R-squared

Analyzing cross-validation metrics

Regularized regression

Regularized regression: Ridge

Lasso regression for feature importance

3

Fine-Tuning Your Model

Having trained models, now you will learn how to evaluate them. In this chapter, you will be introduced to several metrics along with a visualization technique for analyzing classification model performance using scikit-learn. You will also learn how to optimize classification and regression models through the use of hyperparameter tuning.

How good is your model?

Deciding on a primary metric

Assessing a diabetes prediction classifier

Logistic regression and the ROC curve

Building a logistic regression model

The ROC curve

Hyperparameter tuning

Hyperparameter tuning with GridSearchCV

Hyperparameter tuning with RandomizedSearchCV

4

Preprocessing and Pipelines

Learn how to impute missing values, convert categorical data to numeric values, scale data, evaluate multiple supervised learning models simultaneously, and build pipelines to streamline your workflow!

Preprocessing data

Creating dummy variables

Regression with categorical features

Handling missing data

Dropping missing data

Pipeline for song genre prediction: I

Pipeline for song genre prediction: II

Centering and scaling

Centering and scaling for regression

Centering and scaling for classification

Evaluating multiple models

Visualizing regression model performance

Predicting on the test set

Visualizing classification model performance

Pipeline for predicting song popularity

Congratulations

使用 scikit-learn 的监督学习

课程完成

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