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所在平台: Coursera |
课程主页: https://www.coursera.org/learn/supervised-machine-learning-classification
课程评论：没有评论
课程名称：监督机器学习：分类 课程概述：本课程将介绍监督机器学习的主要建模家族之一：分类。您将学习如何训练预测模型以分类离散结果，以及如何使用错误度量来比较不同模型。课程中的动手实践部分将重点关注分类的最佳实践，包括训练和测试分离，以及处理类不平衡的数据集。 课程目标： - 区分分类及分类集成的用途和应用 - 描述并使用逻辑回归模型 - 描述并使用决策树及树集成模型 - 描述并使用其他分类的集成方法 - 使用多种错误度量来比较和选择最适合您数据的分类模型 - 使用过采样和欠采样作为处理数据集中不平衡类的技术 适合人群：本课程面向有志于在商业环境中获得监督机器学习分类技术实践经验的准数据科学家。 技能要求：为了充分利用本课程，您应该熟悉Python开发环境编程，以及具备基础的数据清洗、探索性数据分析、微积分、线性代数、概率和统计知识。 课程大纲： 1. 逻辑回归：深入探讨一种流行的分类算法，并介绍常用错误度量。 2. K最近邻：讲解K最近邻的理论以及使用sklearn构建模型的实践。 3. 支持向量机：了解支持向量机如何通过超平面将数据映射到不同区域。 4. 决策树：介绍决策树的理论和实践示例，强调其优缺点。 5. 集成模型：学习集成模型的优势，以及随机梯度提升等现代方法的应用。 6. 处理不平衡类：讲解使用分层抽样等方法增强模型对不平衡数据集的鲁棒性。 通过本课程，您将获得分类模型的理论知识并积累实际操作经验，为数据科学的职业生涯打下坚实基础。
Part: 1
Title:Logistic Regression
Description:Logistic regression is one of the most studied and widely used classification algorithms, probably due to its popularity in regulated industries and financial settings. Although more modern classifiers might likely output models with higher accuracy, logistic regressions are great baseline models due to their high interpretability and parametric nature. This module will walk you through extending a linear regression example into a logistic regression, as well as the most common error metrics that you might want to use to compare several classifiers and select that best suits your business problem.
Part: 2
Title:K Nearest Neighbors
Description:K Nearest Neighbors is a popular classification method because they are easy computation and easy to interpret. This module walks you through the theory behind k nearest neighbors as well as a demo for you to practice building k nearest neighbors models with sklearn.
Part: 3
Title:Support Vector Machines
Description:This module will walk you through the main idea of how support vector machines construct hyperplanes to map your data into regions that concentrate a majority of data points of a certain class. Although support vector machines are widely used for regression, outlier detection, and classification, this module will focus on the latter.
Part: 4
Title:Decision Trees
Description:Decision tree methods are a common baseline model for classification tasks due to their visual appeal and high interpretability. This module walks you through the theory behind decision trees and a few hands-on examples of building decision tree models for classification. You will realize the main pros and cons of these techniques. This background will be useful when you are presented with decision tree ensembles in the next module.
Part: 5
Title:Ensemble Models
Description:Ensemble models are a very popular technique as they can assist your models be more resistant to outliers and have better chances at generalizing with future data. They also gained popularity after several ensembles helped people win prediction competitions. Recently, stochastic gradient boosting became a go-to candidate model for many data scientists.
Part: 6
Title:Modeling Unbalanced Classes
Description:Some classification models are better suited than others to outliers, low occurrence of a class, or rare events. The most common methods to add robustness to a classifier are related to stratified sampling to re-balance the training data. This module will walk you through both stratified sampling methods and more novel approaches to model data sets with unbalanced classes.
This course introduces you to one of the main types of modeling families of supervised Machine Learning: Classification. You will learn how to train predictive models to classify categorical outcomes and how to use error metrics to compare across different models. The hands-on section of this course focuses on using best practices for classification, including train and test splits, and handling data sets with unbalanced classes. By the end of this course you should be able to: -Differentiate uses and applications of classification and classification ensembles -Describe and use logistic regression models -Describe and use decision tree and tree-ensemble models -Describe and use other ensemble methods for classification -Use a variety of error metrics to compare and select the classification model that best suits your data -Use oversampling and undersampling as techniques to handle unbalanced classes in a data set Who should take this course? This course targets aspiring data scientists interested in acquiring hands-on experience with Supervised Machine Learning Classification techniques in a business setting. What skills should you have? To make the most out of this course, you should have familiarity with programming on a Python development environment, as well as fundamental understanding of Data Cleaning, Exploratory Data Analysis, Calculus, Linear Algebra, Probability, and Statistics.
