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所在平台: Udemy |
课程主页: https://www.udemy.com/course/weka-for-data-mining-and-machine-learning-for-beginners/
课程评论：没有评论
课程名称：实验机器学习与数据挖掘：Weka、MOA和R 课程概述： 本课程旨在帮助学生轻松愉快地学习机器学习。通过使用强大的Weka开源机器学习软件，学生将以简单直观的方式掌握复杂算法的行为。课程内容包括理解各种算法、分类器及其函数，如Naive Bayes、神经网络、J48、OneR、ZeroR、KNN、线性回归和SMO。学生将进行图像、文本和文档分类及数据可视化的实践，并学习如何通过单条命令行将大量文本和HTML文件转换为单个ARFF文件。此外，本课程将探讨监督学习与无监督学习方法的区别，进行实际测试、测验和挑战以加强理解，以及配置和比较分类器的技巧。 课程还涵盖Weka的安装、时间序列和线性回归算法，帮助学生逐步建立预测模型。奖励部分提供了职业技能提升的机会，包括安装MSSQL Server 2017，使用MS TSQL从表中检索数据，以及安装Weka深度学习分类器等。Weka直观的图形用户界面将带领学生从零基础迅速成长为能独立使用机器学习算法的人。 第二部分课程：结合真实世界场景，通过实际实验强调比较不同算法的有效性，培养学生的实践能力。内容包括数据集生成、分类器评估、静态数据集与动态数据流的区别，以及数据挖掘的基本概念。学生将深入了解Hoeffding树分类器的理论基础，并比较批量分类器与增量分类器的优缺点。 课程还有专门章节介绍MOA（大规模在线分析）平台以及情感分析的应用，鼓励学生使用真实的Twitter数据集进行实践，通过Weka对文本数据进行预处理和特征提取。课程结束时，学员将掌握使用Weka、MOA及其他开源工具处理各种数据集、处理数据流、有效评估分类器的能力。 课程内容包括： - 机器学习与数据挖掘的实践应用 - 数据集生成与分类器评估 - 使用Weka和其他开源软件（如R）的整合 - MOA的深入探索 - 基于Weka的情感分析 - 可视化与其他工具的结合 加入我们，开始这段变革性的学习旅程吧！
First Course:This introductory course will help make your machine learning journey easy and pleasant , you will be learning by using the powerful Weka open source machine learning software, developed in New Zealand by the University of Waikato.You will learn complex algorithm behaviors in a straightforward and uncomplicated manner. By exploiting Weka's advanced facilities to conduct machine learning experiments, in order to understand algorithms, classifiers and functions such as ( Naive Bayes, Neural Network, J48, OneR, ZeroR, KNN, linear regression & SMO).Hands-on:Image, text & document classification & Data Visualization How to convert bulk text & HTML files into a single ARFF file using one single command lineDifference between Supervised & Unsupervised Machine Learning methodsPractical tests, quizzes and challenges to reinforce understandingConfiguring and comparing classifiers How to build & configure J48 classifierChallenge & Practical TestsInstalling Weka packages Time Series and Linear Regression AlgorithmWhere do we go from here..The Bonus section (Be a Practitioner and upskill yourself, Installing MSSQL server 2017, Database properties, Use MS TSQL to retrieve data from tables, Installing Weka Deep Learning classifier, Use Java to read arff file, How to integrate Weka API with Java)Weka's intuitive, the Graphical User Interface will take you from zero to hero. You will be learning by comparing different algorithms, checking how well the machine learning algorithm performs till you build your next predicative machine learning model. Second Course: New Course: Machine Learning & Data Mining With Weka, MOA & "R" Open Source Software ToolsHands-On Machine Learning and Data Mining: Practical Applications with Weka, MOA & "R" Open Source Software ToolsDescription:This course emphasizes learning through practical experimentation with real-world scenarios, where different algorithms are compared to determine the most likely one that outperforms others.Welcome to the immersive and practical course on "Hands-On Machine Learning and Data Mining" where you will delve into the world of cutting-edge techniques using powerful open-source tools such as Weka, MOA, "R" and other essential resources. This comprehensive course is designed to equip you with the knowledge and skills needed to excel in the field of data mining and machine learning.Section 1: Data Set Generation and Classifier EvaluationIn this section, you will learn the fundamentals of data set generation, exploring various data types, and understanding the distinction between static datasets and dynamic data streams. You'll delve into the essential aspects of data mining and the evaluation of classifiers, allowing you to gauge the performance of different machine learning models effectively.Section 2: Data Set & Data StreamIn this section, we will explore the fundamental concepts of data set and data stream, crucial aspects of data mining. Understanding the differences between these two data types is essential for selecting the appropriate machine learning approach in different scenarios. Contents are as follows:· What is the Difference between Data Set and Data Stream?· We will begin by demystifying the dissimilarities between static data sets and dynamic data streams.· Data Mining Definition and Applications· We will delve into the definition and significance of data mining, exploring its role in extracting valuable patterns, insights, and knowledge from large datasets. You will gain a clear understanding of the data mining process and how it aids in decision-making and predictive analysis.· Hoeffding Tree Classifier· As an essential component of data stream mining, we will focus on Hoeffding tree classifier. You will learn how this online learning algorithm efficiently handles data streams by making quick and informed decisions based on a statistically sound approach. I will cover the theoretical foundations of the Hoeffding tree classifiers.· Batch Classifier vs. Incremental Classifier· In this part, we will compare batch classifiers with incremental classifiers, emphasizing the strengths and limitations of each approach.· Section 3: Exploring MOA (Massive Online Analysis)In this section, we will take a deep dive into MOA, a powerful platform designed to handle large-scale data streams efficiently. You will learn about the critical differences between batch and incremental settings, and how incremental learning is particularly valuable when dealing with continuous data streams. Additionally, we will conduct comprehensive comparisons of various classifiers and evaluators within MOA, enabling you to identify the most suitable algorithms for specific data scenarios.Section 4: Sentimental Analysis using Weka.This section will focus on Sentimental Analysis, an essential task in natural language processing. We will work with real-world Twitter datasets to classify sentiments using Weka, a versatile machine learning tool. You'll gain hands-on experience in preprocessing textual data and extracting meaningful features for sentiment classification. Moreover, we will integrate open-source resources to augment Weka's capabilities and boost performance.Section 5: A closer look at Massive Online Analysis (MOA).Contents:What is MOA & who is behind it?Open Source Software explainedExperimenting with MOA and WekaSection 6: Integrating open source tools with more Weka packages for machine learning schemes and "R" the statistical programming language.Contents:Install Weka "LibSVM" and "LibLINEAR" packages.Speed comparisonData Visualization with R in WekaUsing Weka to run MLR ClassifiersBy the end of this course, you will have gained the expertise to handle diverse datasets, process data streams, and evaluate classifiers effectively. You will be proficient in using Weka, MOA, and other open-source tools to apply machine learning and data mining techniques in practical applications. So, join us on this journey, and let's embark on a transformative learning experience together!What you'll learn:Practical use of Data Mining Experimenting & Comparing AlgorithmsPreprocess, Classifies, Filters & DatasetsIntegrating open source tools with WekaData Set Generation, Data Set & Data Stream and Classifier EvaluationHow to use Weka with other open source software such as "R"Exploring MOA (Massive Online Analysis)Sentimental Analysis using WekaIntegrating open source tools with more Weka packages for machine learning schemes and "R" the statistical programming language.Optional - Data Science & Data Analytics tools (Install Anaconda, Jupyter Notebook, Neural Network and Deep learning packages)
