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所在平台: Udemy |
课程主页: https://www.udemy.com/course/machine-learning-with-java-and-weka/
课程评论：没有评论
**课程名称：** 使用 Java 和 Weka 进行机器学习 **课程概述：** 本课程旨在教授学员如何利用 Java 编程语言和 Weka 机器学习库进行数据分析和机器学习。课程强调了数据分析和数据科学的重要性和广泛应用，包括提升解决问题的能力、应对高需求市场、理解“数据无处不在”的趋势、认识其日益增长的重要性以及培养跨学科技能。 本课程特别关注如何将 Java 编程与 Weka 库结合，以实现模型的训练和评估。在 CRISP-DM 数据挖掘流程中，机器学习处于建模和评估阶段。课程假定学员已具备基础的 Java 编程知识，并推荐学员先修“创建你的计算器：快速学习 Java 编程基础”课程。 **主要学习内容：** * **数据挖掘基础：** 了解数据挖掘过程，包括数据集的准备、训练集和测试集的划分。 * **Java 和 Weka 集成：** 学习如何在 NetBeans 中创建 Java 应用程序，并集成 Weka 库。 * **回归算法：** 掌握简单线性回归和使用 Weka 和 Java 实现线性回归。 * **聚类算法：** 学习 KMeans 聚类和 Agglomeration 聚类，并在 Weka 和 Java 中实现。 * **分类算法：** 学习并实现 Decision Tree (ID3 算法)、KNN (K-Nearest Neighbors) 和 Naive Bayes 等分类算法。 * **神经网络：** 学习神经网络模型，并在 Weka 和 Java 中应用。 * **算法选择：** 探讨如何选择合适的机器学习算法。 * **模型评估：** 学习如何在 Weka 和 Java 中评估机器学习模型的性能。 * **实战项目：** 引导学员创建自己的数据挖掘软件。 通过本课程的学习，学员将能够熟练运用 Java 和 Weka 构建和评估机器学习模型，为数据科学和物联网等领域打下坚实基础。
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 Java Programming for Machine Learning and Statistical Learning with the Weka library. In CRISP-DM data mining process, machine learning is at the modeling and evaluation stage. You will need to know some Java programming, and you can learn Java programming from my "Create Your Calculator: Learn Java Programming Basics Fast" course. You will learn Java Programming for machine learning and you will be able to train your own prediction models with Naive Bayes, decision tree, knn, neural network, and linear regression, and evaluate your models very soon after learning the course. ContentIntroductionGetting StartedGetting Started 2Getting Started 3Data Mining ProcessData setSplit Training and Testing datasetCreate Java Application using Netbeans with Weka JarSimple Linear RegressionLinear Regression using Weka and JavaLinear Regression using Weka and Java 2Linear Regression using Weka and Java 3KMeans ClusteringKMeans Clustering in Weka and JavaAgglomeration ClusteringAgglomeration Clustering in Weka and JavaDecision Tree ID3 AlgorithmDecision Tree in Weka and JavaKNN ClassificationKNN in Weka and JavaNaive Bayes ClassificationNaive Bayes in Weka and JavaNeural Network ClassificationNeural Network in Weka and JavaWhat Algorithm to Use?Model EvaluationModel Evaluation in Weka and JavaCreate a Data Mining SoftwareCreate a Data Mining Software 2
