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所在平台: Udemy |
课程主页: https://www.udemy.com/course/a00-406-sas-specialist-machine-learning-6-mock-exams/
课程评论：没有评论
Coursera "[A00 - 406] SAS Specialist: Machine Learning- 6 Mock Exams" 课程内容总结： 本课程旨在帮助学员通过 SAS Specialist: Machine Learning (A00-406) 认证考试。课程提供 6 套高质量模拟考试，并附带详尽的答案解析，全面覆盖 SAS Model Studio 和机器学习技术的关键知识点。 **核心目标：** * 掌握 SAS 机器学习基础，包括数据准备、探索和模型构建。 * 理解数据划分、变量选择和模型评估的重要性。 * 学习构建和解析决策树、神经网络、支持向量机和集成模型。 * 高效部署机器学习模型，包括评分和注册模型。 * 运用高级可解释性工具理解复杂的“黑盒”模型。 **主要内容：** * **数据源 (30-36%)：** 数据导入、Model Studio 管道创建与配置、数据划分技术（训练、验证、测试）、事件抽样。 * **数据探索与处理：** 数据探索节点、数据转换、缺失值处理、异常值检测、文本数据处理（Text Mining 节点）、变量选择。 * **模型构建 (40-46%)：** 机器学习基本概念（监督/无监督学习、过拟合/欠拟合、集成模型）、使用决策树、梯度提升、随机森林、神经网络构建预测模型、超参数调优、高级可解释性技术（偏依赖图、ICE 图、LIME、SHAP）。 * **模型评估与部署 (24-30%)：** 模型性能评估（拟合统计量、ROC 曲线、公平性/偏差分析）、SAS Model Studio 中的管道和模型比较、模型部署（导出、注册、发布）、使用评分节点部署模型。 **学员群体：** * 寻求 SAS 认证的数据科学家、机器学习工程师和分析师。 * 希望扩展机器学习工具和技术知识的专业人士。 * 希望通过全球认可的认证提升个人竞争力的个人。 **课程特色：** * 6 套全长模拟考试，每套 60 道题，高度仿真实考环境。 * 每道题提供详尽解释，确保深入理解。 * 通过 SAS Model Studio 节点进行实践操作。 * 全面覆盖 A00-406 考试大纲。 本课程为学员通过 SAS Certified Specialist: Machine Learning 认证考试、并在实践中应用这些技能提供所需工具和知识。
This comprehensive course, "Crack SAS Certified Specialist: Machine Learning Certification," is designed to help you ace the SAS Specialist: Machine Learning (A00-406) certification exam. With 6 quality practice exams and detailed explanations, this unofficial course covers all critical topics needed to master SAS Model Studio and gain expertise in machine learning techniques.By the end of the course, you will have a deep understanding of how to build, assess, and deploy machine learning models using SAS tools and confidently tackle real-world challenges in data science.Course ObjectivesMaster the fundamentals of SAS Machine Learning, including data preparation, exploration, and model building.Understand the importance of data partitioning, variable selection, and model assessment.Build and interpret decision trees, neural networks, support vector machines, and ensemble models.Deploy machine learning models effectively, including scoring and registering models.Use advanced interpretability tools to explain complex "black-box" models.Key Topics CoveredData Sources (30-36%)Setting up and importing data for analysis.Creating Model Studio pipelines and understanding their configurations.Data partitioning techniques for training, validation, and testing.Event-based sampling for handling rare events.Exploring and Modifying DataPreliminary data profiling using the Data Exploration node.Techniques for data transformation, imputation, and outlier detection.Text data preparation with the Text Mining node.Variable selection using supervised and unsupervised methods.Building Models (40-46%)Key machine learning concepts: supervised vs. unsupervised learning, overfitting, underfitting, and ensemble models.Creating predictive models using decision trees, gradient boosting, forests, and neural networks.Tuning hyperparameters like learning rates, regularization, and early stopping criteria.Advanced interpretability techniques: Partial Dependence plots, ICE plots, LIME, and SHAP.Model Assessment and Deployment (24-30%)Evaluating model performance using fit statistics, ROC curves, and fairness/bias analysis.Comparing pipelines and models in SAS Model Studio.Model deployment: exporting, registering, and publishing models.Using scoring nodes to deploy models to production.Who Should Enroll?Data scientists, machine learning engineers, and analysts seeking SAS certification.Professionals looking to expand their knowledge of machine learning tools and techniques.Individuals aiming to enhance their resume with a globally recognized certification.Course Features6 Full-Length Mock Exams with 60 questions each to simulate the certification environment.Detailed Explanations for each question to ensure complete understanding.Hands-On Learning with SAS Model Studio nodes for real-world problem-solving.Comprehensive coverage of the A00-406 exam syllabus.This course provides all the tools and knowledge necessary to excel in the SAS Certified Specialist: Machine Learning certification exam and apply these skills in practical scenarios. Start your journey to becoming a SAS Machine Learning expert today!
