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所在平台: Udemy |
课程主页: https://www.udemy.com/course/introduction-to-ml-classification-models-using-scikit-learn/
课程评论：没有评论
课程名称：使用scikit-learn进行机器学习分类模型入门 课程概述：本课程旨在为您提供机器学习的基本理解，重点关注构建分类模型。课程将解释机器学习的基本概念，包括监督学习和无监督学习、回归与分类以及过拟合等。课程中包含三个实验部分，专注于使用真实数据集构建分类模型，具体技术包括支持向量机、决策树和随机森林。所有实现将使用Python的scikit-learn库进行。该课程适合具备基本Python编程知识的开发者、数据科学家或其他希望学习机器学习并专注于解决分类问题的学习者。
This course will give you a fundamental understanding of Machine Learning overall with a focus on building classification models. Basic ML concepts of ML are explained, including Supervised and Unsupervised Learning; Regression and Classification; and Overfitting. There are 3 lab sections which focus on building classification models using Support Vector Machines, Decision Trees and Random Forests using real data sets. The implementation will be performed using the scikit-learn library for Python. The Intro to ML Classification Models course is meant for developers or data scientists (or anybody else) who knows basic Python programming and wishes to learn about Machine Learning, with a focus on solving the problem of classification.
