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所在平台: Udemy |
课程主页: https://www.udemy.com/course/master-simplified-unsupervised-machine-learning-end-to-end-tm/
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课程名称：掌握高级无监督机器学习全流程 概述：掌握简化的无监督机器学习™是一个全面的项目，旨在深入探讨无监督学习在数据科学和机器学习中的技术、算法和应用。该课程将复杂的无监督学习进行简化，从基础概念到高级的聚类方法、降维和关联规则挖掘，均有涵盖。学习者将获得检测模式、数据分割和揭示隐藏结构的实用技能，无需标记数据，从而为各种行业的实际应用提供强有力的工具。 课程概述： - 课程形式：自我-paced ，辅导课程 - 目标受众：数据科学家、机器学习爱好者以及寻求深入理解无监督学习技术的专业人士 主要学习目标： - 理解无监督学习的核心原理及其应用 - 掌握聚类、异常检测和降维的算法 - 积累PCA、LDA、t-SNE和DBSCAN等高级方法的实践经验 - 应用关联规则挖掘和Apriori算法进行可操作的数据洞察 课程亮点： - 异常检测：在大型数据集中检测离群值和不规则模式 - K-Means和层次聚类：有效数据分割的技术 - 基于密度的聚类DBSCAN：适用于噪声和高密度数据集 - 使用PCA和LDA进行降维：在减少复杂性的同时保留重要数据特征 - t-SNE可视化：将复杂数据转换为直观的二维/三维可视化 - 使用Apriori算法进行关联规则挖掘：揭示隐藏的关联和模式 课程大纲： - 无监督学习与异常检测简介 - K-Means聚类与迭代优化 - 高级聚类 - 层次聚类和树状图 - DBSCAN - 基于密度的聚类及应用 - 主成分分析（PCA） - 特征提取 - 线性判别分析（LDA） - 降维解释 - t-SNE用于数据可视化和降维 - 无监督学习中的模型评估与超参数调优 - 关联规则挖掘 - 购物篮分析、置信度与支持度 - Apriori算法 - 分步讲解与实际应用 通过“掌握简化的无监督机器学习™”，学习者将全面掌握应用无监督技术以挖掘洞察、推动决策和释放数据的全部潜力的能力。 讲师：我们的讲师均为行业领先的AI/ML专家，拥有多年的教学、研究和实际应用经验。他们带来了实践洞察、动手技能和行业最佳实践，使学习过程变得生动且可应用。
Master Simplified Unsupervised Machine Learning™ is a comprehensive program designed to provide a deep dive into the techniques, algorithms, and applications of unsupervised learning in data science and machine learning. This course demystifies the complexity of unsupervised learning, covering everything from foundational concepts to advanced clustering methods, dimensionality reduction, and association rule mining. Learners will gain hands-on skills in detecting patterns, segmenting data, and uncovering hidden structures without labeled data, equipping them with powerful tools for real-world applications across diverse industries.Course OverviewCourse Format: Self-paced with instructor-led sessionsTarget Audience: Data scientists, machine learning enthusiasts, and professionals seeking a deep understanding of unsupervised learning techniquesKey Learning ObjectivesUnderstand the core principles of unsupervised learning and its applicationsMaster algorithms for clustering, anomaly detection, and dimensionality reductionGain practical experience with advanced methods like PCA, LDA, t-SNE, and DBSCANApply association rule mining and the Apriori Algorithm for actionable data insightsCourse HighlightsAnomaly Detection: Detect outliers and irregular patterns within large datasetsK-Means and Hierarchical Clustering: Techniques for segmenting data effectivelyDBSCAN for Density-Based Clustering: Ideal for noisy and high-density datasetsDimensionality Reduction with PCA and LDA: Reduce complexity while preserving essential data featurest-SNE Visualization: Transform complex data for intuitive 2D/3D visualizationsAssociation Rule Mining with Apriori Algorithm: Uncover hidden correlations and patternsCourse CurriculumIntroduction to Unsupervised Learning & Anomaly DetectionK-Means Clustering & Iterative OptimizationAdvanced Clustering - Hierarchical Clustering and DendrogramsDBSCAN - Density-Based Clustering and ApplicationsPrincipal Component Analysis (PCA) - Feature ExtractionLinear Discriminant Analysis (LDA) - Dimensionality Reduction Explainedt-SNE for Data Visualization and Dimensionality ReductionModel Evaluation and Hyperparameter Tuning in Unsupervised LearningAssociation Rule Mining - Market Basket Analysis, Confidence & SupportApriori Algorithm - Step-by-Step Explanation and Practical ApplicationsWith Master Simplified Unsupervised Machine Learning™, learners will be fully equipped to apply unsupervised techniques to uncover insights, drive decisions, and unlock the full potential of data.InstructorOur instructors are industry-leading AI/ML experts with years of experience in teaching, research, and real-world applications. They bring practical insights, hands-on skills, and industry best practices to make learning engaging and applicable.
