| 課程大綱 Syllabus | 
							學生學習目標 Learning Objectives | 
				單元學習活動 Learning Activities | 
				學習成效評量 Evaluation | 
							備註 Notes | 
			
			
			序 No. | 				單元主題 Unit topic | 
				內容綱要 Content summary | 
						
					
				| 1 | 				Course Introduction  | 
				•課程介紹 
•AWS與GCP雲端服務平台介紹  | 					
							•認識AWS/GCP雲端平台之服務與API | 
				
					
										
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				| 2 | 				AWS Fundamentals  | 
				•AWS services and infrastructure 
•Compute Services on AWS 
•Amazon Elastic Compute Cloud (EC2)  | 					
							•認識AWS雲端平台之服務、基礎架構與計算服務 | 
				
					
										
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				| 3 | 				Networking and Storage on AWS  | 
				•Amazon VPC 
•Amazon CloudFront Content Delivery 
•Amazon Elastic Block Storage (EBS) 
•Amazon Simple Storage Service (S3) 
•Amazon Elastic File Service (EFS)  | 					
							•認識AWS網路、內容傳遞與儲存服務 | 
				
					
										
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				| 4 | 				Databases on AWS  | 
				•Amazon Relational Database Service (RDS) 
•Amazon DynamoDB 
•Amazon Redshift 
•Amazon Aurora  | 					
							•認識AWS資料庫之應用服務 | 
				
					
										
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				| 5 | 				Build Serverless Application  | 
				•Creating a Serverless Website with Amazon S3 
•Serverless Computing with AWS Lambda  | 					
							•學習在AWS上建置無伺服器應用 | 
				
					
										
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				| 6 | 				Introducing AWS Machine Learning Services  | 
				•What is machine learning? 
•Business problems solved with machine learning 
•Machine learning process 
•Machine learning tools overview 
•Machine learning challenges 
•Introducing Amazon SageMaker  | 					
							•認識人工智慧、機器學習、深度學習 
•探索企業的ML使用案例 
•了解企業如何使用工具和技術來實現商業目標 
•了解採用AI與ML所面臨的挑戰 | 
				
					
										
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				| 7 | 				Implementing a Machine Learning Pipeline with Amazon SageMaker: Part I  | 
				•Formulating machine learning problems 
•Collecting and securing data 
•Evaluating your data 
•Feature engineering  | 					
							•認識Amazon SageMaker機器學習管線並實作其流程 | 
				
					
										
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				| 8 | 				Implementing a Machine Learning Pipeline with Amazon SageMaker: Part II  | 
				•Training a model 
•Hosting and using the model 
•Evaluating the accuracy of the model 
•Hyperparameter and model tuning  | 					
							•認識Amazon SageMaker機器學習管線並實作其流程 | 
				
					
										
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				| 9 | 				期中考  | 
				測驗與實作報告  | 					
							檢驗上半學期學習成效 | 
				
					
										
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				| 10 | 				Introducing Forecasting  | 
				•Forecasting overview 
•Processing time series data 
•Using Amazon Forecast  | 					
							•認識預測和學習以Amazon Forecast服務解決商業問題 | 
				
					
										
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				| 11 | 				Introducing Natural Language Processing (NLP)  | 
				•Natural language processing managed services 
•Amazon Transcribe 
•Amazon Polly 
•Amazon Translation 
•Comprehend 
•Amazon Lex - Create a chatbot  | 					
							•瞭解與使用 AWS 自然語言處理服務 | 
				
					
										
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				| 12 | 				Amazon managed NLP services Integration Application  | 
				•Amazon Transcribe, Amazon Translation, Amazon Comprehend, Amazon Polly, Amazon Lex之綜合應用  | 					
							•使用Amazon託管之自然語言處理服務之實作 | 
				
					
										
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				| 13 | 				GCP 與 GAE開發環境介紹與建置  | 
				•GCP 雲端平台介紹 
•Google App Engine的設定與SDK安裝  | 					
							•認識Google雲端平台和Google App Engine雲端服務 
•認識以Java 語言開發Google雲端服務使用的軟體及環境建置 | 
				
					
										
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				| 14 | 				GAE的API與功能介紹  | 
				•Google App Engine的API與功能介紹 
•建立一個GAE應用程式 
•在GAE中設計Servlet 
•在GAE中設計JSP  | 					
							•瞭解如何在Eclipse中開發一個簡單的專案 
•熟悉GAE與其開發方式,學習JSP 與Servlet的設計以及在Eclipse中進行執行與測試 | 
				
					
										
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				| 15 | 				GCP之資料儲存  | 
				•Cloud Storage 
•Cloud Datastore 
•Google App Engine API 專題  | 					
							•瞭解GCP之資料儲存模式與實作 | 
				
					
										
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				| 16 | 				GAE之整合範例  | 
				•網頁應用程式服務之設計 
•各項功能與頁面開發 
•完成整合並使用應用程式  | 					
							•整合相關知識並使用JSP、Servlet、Datastore等在專案中實作一個範例系統網頁 | 
				
					
										
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				| 17 | 				期末專案分組報告(一)  | 
				•AWS/Google雲端服務系統專題之設計與實作報告及討論  | 					
							•學習運用所學技術於專案之合作開發 | 
				
					
										
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				| 18 | 				期末專案分組報告(二)  | 
				•AWS/Google雲端服務系統專題之設計與實作報告及討論  | 					
							•學習運用所學技術於專案之合作開發 | 
				
					
										
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