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所在平台: Udemy |
课程主页: https://www.udemy.com/course/machine-learning-basics-with-minitab/
课程评论：没有评论
**课程名称：** 使用 Minitab 进行机器学习基础 **课程概述：** 本课程为期全面，旨在通过 Minitab 深入讲解机器学习的基础知识，重点关注监督学习。课程涵盖了回归分析和二元逻辑分类的基本概念，以及如何评估模型和解读结果。此外，课程还介绍了用于二元和多项分类的基于树的模型。 课程从机器学习介绍开始，帮助学生理解机器学习的定义、不同类型以及监督学习与无监督学习的区别。随后，概述了监督学习的基础，包括学习方法、不同类型的回归，以及在机器学习与经典统计学中使用回归模型所需满足的条件。 接着，课程详细阐述了回归分析，介绍了不同类型的回归模型以及如何使用 Minitab 进行评估。重点讲解了统计上显著的预测变量、多重共线性，以及如何处理包含分类预测变量的回归模型，包括加法效应和交互效应。学生还将学习如何使用置信区间和预测区间对新观测值进行预测。 之后，课程进入模型构建阶段，学生将学习如何处理具有“错误”预测变量的回归方程，并使用 Minitab 中的逐步回归找到最优模型，包括模型评估和结果解读。 课程随后转向二元逻辑回归，用于二元分类。学生将学习如何评估二元分类模型，包括 ROC 曲线和 AUC 等拟合优度指标，并使用 Minitab 通过二元逻辑回归分析心脏衰竭数据集。 接着，课程讲解了分类树，包括通过误分类率、基尼不纯度和熵等节点划分方法的概述。学生将学习如何预测节点类别，并使用误分类成本、ROC 曲线、增益图和提升图评估模型的好坏，适用于二元和多项分类。 最后，课程介绍了预定义先验概率和输入误分类成本的概念及用法，以及如何使用 Minitab 构建决策树。整个课程过程中，学生将通过实践获得将所学概念应用于真实场景的经验。 总而言之，本课程通过 Minitab 提供了对机器学习基础知识的全面理解，重点关注监督学习、回归分析和分类。完成课程后，学生将掌握运用监督机器学习技术解决实际数据问题的知识和技能。
Course Title: Machine Learning Basics with MinitabCourse Description:This comprehensive course is designed to provide a detailed understanding of the basics of machine learning using Minitab, with a focus on supervised learning. The course covers the fundamental concepts of regression analysis and binary logistic classification, including how to evaluate models and interpret results. The course also covers tree-based models for binary and multinomial classification.The course begins with an introduction to machine learning, where students will gain an understanding of what machine learning is, the different types of machine learning, and the difference between supervised and unsupervised learning. This is followed by an overview of the basics of supervised learning, including how to learn, the different types of regression, and the conditions that must be met to use regression models in machine learning versus classical statistics.The course then delves into regression analysis in detail, covering the different types of regression models and how to use Minitab to evaluate them. This includes a thorough explanation of statistically significant predictors, multicollinearity, and how to handle regression models that include categorical predictors, including additive and interaction effects. Students will also learn how to make predictions for new observations using confidence intervals and prediction intervals.Next, the course moves onto model building, where students will learn how to handle regression equations with "wrong" predictors and use stepwise regression to find optimal models in Minitab. This includes an overview of how to evaluate models and interpret results.The course then shifts to binary logistic regression, which is used for binary classification. Students will learn how to evaluate binary classification models, including good fit metrics such as the ROC curve and AUC. They will also use Minitab to analyze a heart failure dataset using binary logistic regression.The course then covers classification trees, including an overview of node splitting methods such as splitting by misclassification rate, Gini impurity, and entropy. Students will learn how to predict class for a node and evaluate the goodness of the model using misclassification costs, ROC curve, Gain chart, and Lift chart for both binary and multinomial classification.Finally, the course covers the concept and use of predefined prior probabilities and input misclassification costs, and how to build a tree using Minitab. Throughout the course, students will gain hands-on experience applying the concepts learned in real-world scenarios.Overall, this course provides a thorough understanding of machine learning basics using Minitab, with a focus on supervised learning, regression analysis, and classification. Upon completion of this course, students will have the knowledge and skills to apply supervised machine learning techniques to real-world data problems.
