Chevron Left
AI Workflow: Business Priorities and Data Ingestion に戻る

IBM による AI Workflow: Business Priorities and Data Ingestion の受講者のレビューおよびフィードバック

4.3
145件の評価

コースについて

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....

人気のレビュー

フィルター:

AI Workflow: Business Priorities and Data Ingestion: 1 - 25 / 33 レビュー

by Yifan Z

2020年2月16日

by Tracy P

2020年2月22日

by L L

2020年1月10日

by Jonathan V

2020年5月23日

by Armen M

2020年4月11日

by Nagendra P P

2020年8月21日

by Иокша Д С

2021年1月31日

by Paulo C C

2021年1月3日

by Pascal U E

2021年2月17日

by Raja N

2020年7月13日

by Neela M

2020年7月17日

by Oliver M R

2020年6月23日

by Dino H

2021年9月16日

by Laurent V

2020年7月16日

by Yuliia H

2020年7月28日

by Julio C

2020年7月10日

by Mohamed A G A

2021年9月15日

by PARITOSH P

2020年7月2日

by Zeghraoui M

2021年2月2日

by Abrar J

2020年5月7日

by Don W

2020年2月16日

by FARHAN K

2021年3月20日

by BHAVANA g

2020年8月17日

by Shen H

2020年12月15日

by Sourav D

2020年5月28日