Learners will be able to explain core AI concepts, differentiate machine learning techniques, analyze AWS AI services, apply model training workflows, and evaluate end-to-end AI solutions using real-world examples. This course equips participants with a complete understanding of Artificial Intelligence fundamentals while developing practical cloud skills using AWS tools such as SageMaker, Comprehend, Rekognition, Lex, and Polly.

您将学到什么
Explain core AI and machine learning concepts and differentiate common ML techniques.
Use AWS AI services to build, train, and deploy intelligent applications.
Design end-to-end AI solutions and prepare for the AWS Certified AI Practitioner exam.
您将获得的技能
- Reinforcement Learning
- AWS SageMaker
- Machine Learning
- Prompt Engineering
- Artificial Intelligence
- Computer Vision
- Model Deployment
- Natural Language Processing
- Responsible AI
- Amazon Web Services
- Image Analysis
- Supervised Learning
- Data Preprocessing
- Unsupervised Learning
- Deep Learning
- Model Evaluation
- 技能部分已折叠。显示 9 项技能,共 16 项。
要了解的详细信息

添加到您的领英档案
16 项作业
January 2026
了解顶级公司的员工如何掌握热门技能

积累特定领域的专业知识
- 向行业专家学习新概念
- 获得对主题或工具的基础理解
- 通过实践项目培养工作相关技能
- 获得可共享的职业证书

该课程共有4个模块
This module introduces the fundamental principles of Artificial Intelligence and Machine Learning, covering key concepts such as NLP, Computer Vision, learning paradigms, and essential analytical techniques. Learners develop a strong conceptual foundation to support deeper exploration of AI applications and AWS-based ML workflows.
涵盖的内容
9个视频4个作业
This module explores the AWS AI ecosystem, introducing essential cloud-based AI services such as SageMaker, Comprehend, DeepLens, and foundational implementation patterns. Learners gain hands-on understanding of how AWS accelerates AI development through managed services, automation, and real-world case studies.
涵盖的内容
9个视频4个作业
This module focuses on building conversational, vision-based, and multi-service AI solutions using AWS. Learners gain experience integrating Lex, Polly, Rekognition, and other services to design intelligent, end-to-end cloud applications while exploring foundation models and model engineering best practices.
涵盖的内容
7个视频4个作业
This module covers the complete ML lifecycle, including model training, optimization, evaluation, deployment, ethical AI practices, prompt engineering, and continuous improvement strategies. Learners also prepare for certification through exam-focused insights and advanced scenario-based analysis.
涵盖的内容
8个视频4个作业
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