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所在平台: Udemy |
课程主页: https://www.udemy.com/course/machine-learning-modelling-with-rapidminer/
课程评论：没有评论
**课程名称：** 使用 RapidMiner 进行机器学习建模 **课程概述：** 本课程是一门直观且全面的机器学习入门课程，专注于使用 RapidMiner 这一强大的平台进行实际的人工智能应用开发。您将获得使用 RapidMiner 构建、训练和评估机器学习模型的实践经验。 课程内容涵盖了广泛的机器学习模型，包括监督学习和无监督学习技术。具体内容包括： * **监督学习：** 线性回归、决策树、集成技术（ensemble techniques）、神经网络等，用于回归和分类任务的预测。 * **无监督学习：** 聚类（clustering）、降维（dimensionality reduction）等。 * **推荐系统：** 学习构建基于排名的技术、协同过滤方法（用户-用户、项-项、矩阵分解等）以及基于内容的推荐系统。 此外，课程还将教授如何评估和优化模型，通过数据驱动技术提升模型性能。 **课程成果：** 完成本课程后，您将能够： * 熟练使用 RapidMiner 构建机器学习模型。 * 构建和训练监督学习模型，以完成回归和分类任务的预测。 * 成功构建和训练神经网络。 * 掌握机器学习开发的最佳实践，确保模型能够良好地泛化到新的、未见过的数据。 * 构建和使用决策树以及集成方法。 * 运用聚类和降维等无监督学习算法。 * 利用多种技术构建推荐系统。 通过本课程的学习，您将对核心机器学习概念有深刻的理解，并掌握实用的技能，从而能够自信、快速地应用算法解决复杂的现实世界问题。
This intuitive program comprehensively introduces machine learning fundamentals and practical AI application development using RapidMiner.You'll gain hands-on experience in building, training, and evaluating machine learning models with RapidMiner.The course covers a wide range of machine learning models, including both supervised and unsupervised techniques, such as linear regression, neural networks, decision trees, ensemble techniques, neural networks, clustering, dimensionality reduction, and recommender systems.In addition, you'll develop the skills to evaluate and fine-tune models, enhance performance through data-driven techniques, and more.By the end of this program, you will have a strong grasp of core machine learning concepts and practical skills, enabling you to confidently and quickly apply algorithms to solve complex, real-world challenges.After completing this course, you will be capable of:• Work with RapidMiner to build machine learning models.• Build and train supervised machine learning models for prediction in regression and classification tasks.• Build and train a neural network.• Utilize machine learning development best practices to ensure that your models generalize well to new and unseen data.• Build and use decision trees and ensemble methods.• Use unsupervised learning algorithms such as clustering and dimensionality reduction.• Build recommender systems with rank-based techniques, collaborative filtering approach (user-user, item-item, matric decomposition,...), and content-based method.
