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學生對 IBM 提供的 AI Workflow: Business Priorities and Data Ingestion 的評價和反饋

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課程概述

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....

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1 - AI Workflow: Business Priorities and Data Ingestion 的 25 個評論(共 33 個)

創建者 Yifan Z

2020年2月16日

創建者 Tracy P

2020年2月22日

創建者 L L

2020年1月10日

創建者 Jonathan V

2020年5月23日

創建者 Armen M

2020年4月11日

創建者 Nagendra P P

2020年8月21日

創建者 Иокша Д С

2021年1月31日

創建者 Paulo C C

2021年1月3日

創建者 Pascal U E

2021年2月17日

創建者 Raja N

2020年7月13日

創建者 Neela M

2020年7月17日

創建者 Oliver M R

2020年6月23日

創建者 Dino H

2021年9月16日

創建者 Laurent V

2020年7月16日

創建者 Yuliia H

2020年7月28日

創建者 Julio C

2020年7月10日

創建者 Mohamed A G A

2021年9月15日

創建者 PARITOSH P

2020年7月2日

創建者 Zeghraoui M

2021年2月2日

創建者 Abrar J

2020年5月7日

創建者 Don W

2020年2月16日

創建者 FARHAN K

2021年3月20日

創建者 BHAVANA g

2020年8月17日

創建者 Shen H

2020年12月15日

創建者 Sourav D

2020年5月28日