|
所在平台: Udemy |
课程主页: https://www.udemy.com/course/aws-certified-machine-learning-specialty-official/
课程评论：没有评论
课程名称：AWS认证机器学习专业人员 2025年第一次考试！ 课程概述： 本课程旨在帮助学员获得AWS认证机器学习专业人员资格，采用模拟试题的形式，课程包含329道与真实考试相似的问答。每个正确答案都附有详细解释，深入帮助学员理解相关概念。即使是错误答案，也提供了详细解释，确保每次失误都成为宝贵的学习机会。此外，课程还推荐外部参考资料，以便学员进一步深入研究所涉及的主题。获得此认证可以帮助组织识别和培养实施云计算项目所需的关键技能人才，证明在AWS上构建、训练、调整和部署机器学习（ML）模型的专业能力。 认证获取要求： 要获得该认证，考生需参加并通过AWS认证机器学习专业人员考试（MLS-C01）。考试题型包括多项选择题和多响应题。 目标受众： 本课程面向以下群体： - 学生 - 顾问 - IT主管 - IT经理 - IT团队领导 - IT专业人士 学习内容： 1. 机器学习模型开发：在AWS机器学习服务、框架和库（如Amazon SageMaker、TensorFlow和Apache MXNet）上开发和实现机器学习模型的能力。 2. 数据准备与特征工程：掌握数据预处理、清理及特征工程技术，以准备机器学习任务的数据，包括数据整理、特征选择和转换。 3. 模型训练与优化：具备在AWS上训练和优化机器学习模型的能力，包括选择适当的算法、调整超参数和优化模型性能与准确度。 4. 模型部署与管理：了解如何在AWS上将机器学习模型部署到生产环境，包括将模型作为端点部署、管理模型版本和监测模型性能与推理。 5. 机器学习运维（MLOps）：掌握MLOps原则及在AWS上实现机器学习生命周期自动化与优化的最佳实践，包括模型版本管理、测试、部署自动化及持续集成/持续部署（CI/CD）流水线。 总之，该课程为希望深入了解AWS机器学习技术并获得认证的学员提供了全面的学习资料和实践机会。
SIMULATORS 2025!!. Become a AWS Certified Machine Learning Specialty in First Attempt. This course consists of 329 questions and answers like the real exam!!. Every correct answer comes with a thorough explanation to ensure you understand the concept in-depth.Even incorrect answers are accompanied by detailed explanations, turning each error into a valuable learning opportunityExpand your understanding with recommended external references. We provide additional resources for you to delve even deeper into the covered topics.This credential helps organizations identify and develop talent with critical skills for implementing cloud initiatives. Earning AWS Certified Machine Learning - Specialty validates expertise in building, training, tuning, and deploying machine learning (ML) models on AWS.What does it take to earn this certification?To earn this certification, you'll need to take and pass the AWS Certified Machine Learning - Specialty exam (MLS-C01). The exam features a combination of two question formats: multiple choice and multiple response.Audience Profile:The target audience includes:- Students- Consultants- IT Directors- IT Managers- IT Team Leaders- IT ProfessionalsWhat you'll learnMachine Learning Model Development: Ability to develop and implement machine learning models using AWS machine learning services, frameworks, and libraries, such as Amazon SageMaker, TensorFlow, and Apache MXNet.Data Preparation and Feature Engineering: Proficiency in data preprocessing, cleaning, and feature engineering techniques to prepare data for machine learning tasks, including data wrangling, feature selection, and transformation.Model Training and Optimization: Competence in training and optimizing machine learning models on AWS, including selecting appropriate algorithms, tuning hyperparameters, and optimizing model performance and accuracy.Model Deployment and Management: Knowledge of deploying machine learning models into production environments on AWS, including deploying models as endpoints, managing model versions, and monitoring model performance and inference.Machine Learning Operations (MLOps): Understanding of MLOps principles and best practices for automating and streamlining the end-to-end machine learning lifecycle on AWS, including model versioning, testing, deployment automation, and continuous integration/continuous deployment (CI/CD) pipelines.
