Fundamentals of AWS AI and ML Solutions

位教师：Whizlabs Instructor

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3个模块

深入了解一个主题并学习基础知识。

初级等级

推荐体验

7 小时完成

灵活的计划

自行安排学习进度

3个模块

深入了解一个主题并学习基础知识。

初级等级

推荐体验

7 小时完成

灵活的计划

自行安排学习进度

您将学到什么

Understand the complete machine learning lifecycle, from data preparation to evaluation.
Learn to prepare, manage, and operationalize ML data and features using SageMaker Data Wrangler, Feature Store, and Model Monitor.
Identify the right AWS AI service for common business and application use cases.

您将获得的技能

Amazon Web Services
Text Mining
Machine Learning
Feature Engineering
MLOps (Machine Learning Operations)
Data Wrangling
Natural Language Processing
Applied Machine Learning
Image Analysis
Model Deployment
AWS SageMaker
Model Evaluation
Artificial Intelligence and Machine Learning (AI/ML)
Deep Learning
AI Personalization
Computer Vision
技能部分已折叠。显示 9 项技能，共 16 项。

要了解的详细信息

可分享的证书

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作业

6 项作业

授课语言：英语（English）

了解顶级公司的员工如何掌握热门技能

了解关于 Coursera for Business 的更多信息

Petrobras, TATA, Danone, Capgemini, P&G 和 L'Oreal 的徽标

该课程共有3个模块

Fundamentals of AWS AI and ML Solutions course is designed for cloud engineers, developers, and technical professionals who want to build a strong foundation in artificial intelligence (AI), machine learning (ML), and deep learning using AWS services. The course focuses on helping learners understand how machine learning systems work, how to identify the right ML approach for real-world problems, and how to use managed AWS AI/ML services to accelerate solution development.

Learners will progress from core AI and ML fundamentals to hands-on exposure with Amazon SageMaker and a wide range of AWS AI services used for language, vision, speech, and intelligent automation use cases. The course comprises approximately 4–5 hours of video content, organised into three comprehensive modules, each divided into focused technical lessons. To reinforce learning, each module includes quizzes and in-video knowledge checks, allowing learners to validate both conceptual understanding and practical knowledge as they progress. - Module 1: Fundamentals of AI & ML - Module 2: Amazon Sagemaker - Module 3: AWS Managed AI Services By the end of the course, learners will be confident in understanding ML workflows, evaluating models, and choosing the right AWS AI services for business and technical requirements. - Understand how ML and deep learning models are operationalized in production systems. - Learn to prepare, manage, and operationalize ML data and features using SageMaker Data Wrangler, Feature Store, and Model Monitor. - Apply human-in-the-loop workflows to improve accuracy and reliability.

Welcome to Week 1 of the Fundamentals of AWS AI and ML Solutions course. In this week, you will build a strong conceptual foundation in artificial intelligence and machine learning, starting with a clear understanding of what machine learning is and how it differs from artificial intelligence and deep learning.You will explore the types of data used in machine learning systems and examine the major categories of machine learning. This week also introduces you to AWS services for machine learning, providing an overview of how managed services from Amazon Web Services support model development, training, and deployment. As part of model evaluation, you will learn how to analyze classification results using confusion matrices, interpret their outcomes, and apply evaluation metrics for both classification and regression problems. The week concludes with an introduction to deep learning, followed by a discussion on how machine learning and deep learning models are used in production environments, including key considerations such as scalability, performance, and reliability.

涵盖的内容

16个视频2篇阅读材料2个作业

16个视频总计100分钟

What is Machine Learning? 5分钟
Understanding difference - AI Vs Deep Learning Vs Machine Learning 3分钟
Types of Data 8分钟
Types of Machine Learning 5分钟
Example Use Cases to Identify the Machine Learing Use Case 8分钟
Steps for Machine Learning 7分钟
AWS Services for Machine Learning 6分钟
Classification task - Demo 11分钟
Model Selection, Training and Evaluation 7分钟
Data Preprocessing Essentials 6分钟
Evaluating Classification Models 5分钟
Evaluation Metrics - Regression 6分钟
Unsupervised Learning - Clustering 5分钟
Types of Inferencing - When to Use What ? 9分钟
What is Deep Learning ? 5分钟
Usage of Deep Learning/ ML models in Production 5分钟

2篇阅读材料总计25分钟

Welcome to the Course 15分钟
Overview of Fundamentals of AI & ML 10分钟

2个作业总计50分钟

Fundamentals of AI & ML - Assessment 25分钟
Features of AI and ML - Knowledge Check 25分钟

Welcome to Week 2. In this week, you will be introduced to Amazon SageMaker, AWS’s fully managed service for building, training, and deploying machine learning models at scale. You will begin with an overview of SageMaker and explore its core components through hands-on demonstrations. As the module progresses, you will take a deep dive into essential SageMaker capabilities such as Data Wrangler for data preparation, Feature Store for managing and reusing features, and Model Monitor for detecting data drift and maintaining model performance in production. By the end of this module, you will be able to confidently navigate the SageMaker ecosystem, prepare and manage ML data efficiently, operationalize features, monitor deployed models, and accelerate machine learning development using built-in templates and pretrained models.

涵盖的内容

6个视频1篇阅读材料2个作业

6个视频总计43分钟

Introduction to Amazon Sagemaker 4分钟
Amazon Sagemaker - Demo 11分钟
Amazon Sagemaker Data Wrangler 7分钟
Amazon Sagemaker Feature Store - Deep Dive 8分钟
Amazon Sagemaker Model Monitor - Deep Dive 9分钟
Amazon Sagemaker Jumpstart 5分钟

1篇阅读材料总计10分钟

Overview of Amazon Sagemaker 10分钟

2个作业总计50分钟

Amazon Sagemaker - Assessment 25分钟
Amazon Sagemaker - Knowledge Check 25分钟

Welcome to Week 3 of the Fundamentals of AWS AI and ML Solutions course. In this module, you will begin by working with language-based AI services such as Amazon Comprehend, Amazon Translate, and Amazon Transcribe, learning how to extract insights from text, translate content across languages, and convert speech into text. The module then expands into speech and vision capabilities using Amazon Polly and Amazon Rekognition, enabling you to generate lifelike speech and analyze images and videos for faces, objects, and content moderation.You will also explore conversational and search-based AI solutions with Amazon Lex and Amazon Kendra. The module also covers personalization and document intelligence through Amazon Personalize and Amazon Textract, demonstrating how AWS AI services can be used to deliver tailored user experiences and extract structured data from scanned documents.