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所在平台: Udemy |
课程主页: https://www.udemy.com/course/master-classification-and-feedforward-networks-2024/
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课程名称：掌握分类与前馈网络 [2025] 概述： 欢迎参加《掌握分类与前馈网络》课程！分类和监督学习是数据科学、机器学习、建模和人工智能中最重要和常见的任务之一。本视频课程将教您掌握分类和监督学习，包括一系列先进的分类技术，如XGBoost分类器。您将通过实践分类理论，轻松自信地执行高级分类任务。 您将学习的内容包括： - 理论与实践双重掌握分类和监督学习 - 了解从逻辑回归、线性判别分析到XGBoost分类器和高斯朴素贝叶斯分类器的各种分类模型 - 使用先进的决策树、随机森林和投票分类器模型 - 应用前馈多层人工神经网络及其高级分类模型结构 - 利用增强的决策表面图和其他图形工具评估分类器的性能 - 使用Scikit-learn库进行分类，并辅以Matplotlib、Seaborn、Pandas和Python的学习 - 云计算：使用Anaconda Cloud Notebook，掌握云计算资源 - 选项：使用Anaconda分发版（适用于Windows、Mac、Linux） - 选项：使用Conda包管理系统与命令行安装/更新库和软件包的Python基本环境 课程适合： - 希望掌握分类和监督学习的各类学习者 - 具备数据科学或机器学习基础的人士 - 希望学习高级分类技能的人员 这门课程是学习掌握分类、前馈网络和监督学习的绝佳途径，并且是我们自己希望能参与的课程。如果有时间机器，我们会选择报名参加这门课程。从课程中您将获得超过5小时的视频教程，以及完成课程后颁发的结业证书。 课程要求： - 熟悉使用Windows、MacOS、iOS、Android、ChromeOS或Linux的日常计算机操作 - 基本的Python和Pandas技能 - 需要访问互联网的计算机 现在就报名，您将获得全面的学习体验！
Welcome to the course Master Classification and Feedforward Networks!Classification and Supervised Learning are one of the most important and common tasks for Data Science, Machine Learning, modeling, and AI.This video course will teach you to master Classification and Supervised Learning with a number of advanced Classification techniques such as the XGBoost Classifier. You will learn to use practical classification hands-on theory and learn to execute advanced Classification tasks with ease and confidence.You will learn to use Classification models such as Logistic Regression, Linear Discriminant Analysis, Gaussian Naïve Bayes Classifier models, Decision Tree Classifiers, Random Forest Classifiers, and Voting Classifier modelsYou will learn to handle advanced model structures such as feedforward artificial neural networks for classification tasks and to use effective augmented decision surfaces graphs and other graphing tools to assist in judging Classifier performanceYou will learn to:Master Classification and Supervised Learning both in theory and practiceMaster Classification models from Logistic Regression and Linear Discriminant Analysis to the XGBoost Classifier, and the Gaussian Naïve Bayes Classifier modelUse practical classification hands-on theory and learn to execute advanced Classification tasks with ease and confidenceUse advanced Decision Tree, Random Forest, and Voting Classifier modelsUse Feedforward Multilayer Artificial Neural Networks and advanced Classifier model StructuresUse effective augmented decision surfaces graphs and other graphing tools to judge Classifier performanceUse the Scikit-learn library for Classification supported by Matplotlib, Seaborn, Pandas, and PythonCloud computing: Use the Anaconda Cloud Notebook (Cloud-based Jupyter Notebook). Learn to use Cloud Computing resources.Option: To use the Anaconda Distribution (for Windows, Mac, Linux)Option: Use Python environment fundamentals with the Conda package management system and command line installing/updating of libraries and packages - golden nuggets to improve your quality of work life.And much more…This course is an excellent way to learn to master Classification, feedforward Networks, and Supervised Learning for ClassificationThis course is designed for everyone who wants tolearn to master Classification and Supervised Learninglearn to master Classification and Supervised Learning and knows Data Science or Machine Learninglearn advanced Classification skillsThis course is a course we ourselves would want to be able to enroll in if we could time-travel and become new students. In our opinion, this course is the best course to learn to master Classification.Course requirements:Everyday experience using a computer with either Windows, MacOS, iOS, Android, ChromeOS, or Linux is recommendedBasic Python and Pandas skillsAccess to a computer with an internet connectionThe course only uses costless softwareWalk-you-through installation and setup videos for Cloud computing and Windows 10/11 is includedEnroll now to receive 5+ hours of video tutorials with manually edited English captions, and a certificate of completion after completing the course!
