AI Workflow: Business Priorities and Data Ingestion
1.符合資格者將於出貨後三個工作日陸續發送交易訊息通知。2.點數將於廠商出貨後，隔天起算110天後陸續確認發送。3.國際商家之商品金額及回饋點數依據將以商品未稅價格為準。4.國際商家之商品金額可能受匯率影響而有微幅差異。5.禮品卡支付以及使用未授權優惠碼不符合贈點資格。6.點數發送依據及返點上限將以「訂單總金額」計算（不含運費及稅額），不論訂單中有多少商品，於LINE購物皆視為只購買一商品（金額為當筆訂單所有商品加總金額），亦即點數回饋計算並非以coursera實際購買商品數量拆分計算 。7. 同6說明，訂單完成後的顯示金額可能包含部分運費或稅金，可返點金額將以系統回傳金額為準 8.若於商家App下單，不符合LINE購物導購資格。商品描述
This is the first course of a six part specialization. You are STRONGLY encouraged to complete these courses in order as they are not individual independent courses, but part of a workflow where each course builds on the previous ones. This first course in the IBM AI Enterprise Workflow Certification specialization introduces you to the scope of the specialization and prerequisites. Specifically, the courses in this specialization are meant for practicing data scientists who are knowledgeable about probability, statistics, linear algebra, and Python tooling for data science and machine learning. A hypothetical streaming media company will be introduced as your new client. You will be introduced to the concept of design thinking, IBMs framework for organizing large enterprise AI projects. You will also be introduced to the basics of scientific thinking, because the quality that distinguishes a seasoned data scientist from a beginner is creative, scientific thinking. Finally you will start your work for the hypothetical media company by understanding the data they have, and by building a data ingestion pipeline using Python and Jupyter notebooks. By the end of this course you should be able to: 1. Know the advantages of carrying out data science using a structured process 2. Describe how the stages of design thinking correspond to the AI enterprise workflow 3. Discuss several strategies used to prioritize business opportunities 4. Explain where data science and data engineering have the most overlap in the AI workflow 5. Explain the purpose of testing in data ingestion 6. Describe the use case for sparse matrices as a target destination for data ingestion 7. Know the initial steps that can be taken towards automation of data ingestion pipelines Who should take this course? This course targets existing data science practitioners that have expertise building machine learning models, who want to deepen their skills on building and deploying AI in large enterprises. If you are an aspiring Data Scientist, this course is NOT for you as you need real world expertise to benefit from the content of these courses. What skills should you have? It is assumed you have a solid understanding of the following topics prior to starting this course: Fundamental understanding of Linear Algebra; Understand sampling, probability theory, and probability distributions; Knowledge of descriptive and inferential statistical concepts; General understanding of machine learning techniques and best practices; Practiced understanding of Python and the packages commonly used in data science: NumPy, Pandas, matplotlib, scikit-learn; Familiarity with IBM Watson Studio; Familiarity with the design thinking process.