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所在平台: Udemy |
课程主页: https://www.udemy.com/course/learn-machine-learning-with-weka/
课程评论：没有评论
**课程名称：** 使用 Weka 学习机器学习 **课程概述：** 本课程旨在介绍数据分析和数据科学的重要性，并教授如何使用 Weka 这一强大的数据挖掘软件进行机器学习。 **为何学习数据分析和数据科学？** * **提升解决问题的能力：** 培养分析性思维，以正确的方式处理问题，这在职业生涯和日常生活中都极具价值。 * **高需求职业：** 数据分析师和数据科学家需求旺盛，随着各行业对数据的依赖增加，其价值将持续攀升。 * **分析无处不在：** 数据已渗透到各个领域，所有公司都需要从中获取洞察以改进流程，这是一个激动人心的职业发展时机。 * **重要性日益凸显：** 随着海量数据的涌现，企业从数据中发掘洞察以辅助决策的机会前所未有，数据分析师的价值和就业前景将更加广阔。 * **涵盖多元技能：** 数据分析领域融合了计算机科学、商业和数学等多种学科，数据分析师还需具备清晰沟通复杂信息的能力。 **课程内容亮点：** 本课程是学习 Weka 和机器学习的入门课程，重点关注 CRISP 数据挖掘流程中的模型构建和评估。您将学习以下核心机器学习算法： * 线性回归 (Linear Regression) * K-均值聚类 (K-means Clustering) * 层次聚类 (Agglomeration Clustering) * K-近邻算法 (KNN) * 朴素贝叶斯 (Naive Bayes) * 神经网络 (Neural Network) **课程结构：** 课程内容涵盖从基础的 Weka 操作到具体算法的实现和应用，包括： * Weka 入门与基础操作 * 数据挖掘流程详解 * 简单线性回归及在 Weka 中的应用 * K-均值与层次聚类算法及其在 Weka 中的应用 * 决策树（ID3 算法）及在 Weka 中的应用 * K-近邻分类及其在 Weka 中的应用 * 朴素贝叶斯算法及其在 Weka 中的应用 * 如何选择合适的算法 * 模型评估方法 * Weka 高级功能：属性选择、数据可视化、模型选择与部署 通过本课程的学习，您将能够掌握使用 Weka 进行数据分析和机器学习的基本技能，并为进一步深入理解相关领域打下坚实基础。
Why learn Data Analysis and Data Science?According to SAS, the five reasons are1. Gain problem solving skillsThe ability to think analytically and approach problems in the right way is a skill that is very useful in the professional world and everyday life. 2. High demandData Analysts and Data Scientists are valuable. With a looming skill shortage as more and more businesses and sectors work on data, the value is going to increase. 3. Analytics is everywhereData is everywhere. All company has data and need to get insights from the data. Many organizations want to capitalize on data to improve their processes. It's a hugely exciting time to start a career in analytics.4. It's only becoming more importantWith the abundance of data available for all of us today, the opportunity to find and get insights from data for companies to make decisions has never been greater. The value of data analysts will go up, creating even better job opportunities. 5. A range of related skillsThe great thing about being an analyst is that the field encompasses many fields such as computer science, business, and maths. Data analysts and Data Scientists also need to know how to communicate complex information to those without expertise.The Internet of Things is Data Science + Engineering. By learning data science, you can also go into the Internet of Things and Smart Cities. This is the bite-size course to learn Weka and Machine Learning. You will learn Machine Learning which is the Model and Evaluation of the CRISP Data Mining Process. You will learn Linear Regression, Kmeans Clustering, Agglomeration Clustering, KNN, Naive Bayes, and Neural Network in this course. ContentGetting StartedGetting Started 2Data Mining ProcessSimple Linear RegressionRegression in WekaKMeans ClusteringKMeans Clustering in WekaAgglomeration ClusteringAgglomeration Clustering in WekaDecision Tree: ID3 AlgorithmDecision Tree in WekaKNN ClassificationKNN in WekaNaive BayesNaive Bayes in WekaWhat Algorithm to use?Model EvaluationWeka Advanced Attribute SelectionWeka Advanced Data VisualizationsWeka Model Selection and Deployment
