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Voltar para AI Workflow: Business Priorities and Data Ingestion

Comentários e feedback de alunos de AI Workflow: Business Priorities and Data Ingestion da instituição IBM

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Sobre o curso

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 — 25 de 33 Avaliações para o AI Workflow: Business Priorities and Data Ingestion

por Yifan Z

16 de fev de 2020

por Tracy P

22 de fev de 2020

por L L

10 de jan de 2020

por Jonathan V

23 de mai de 2020

por Armen M

11 de abr de 2020

por Nagendra P P

21 de ago de 2020

por Иокша Д С

31 de jan de 2021

por Paulo C C

3 de jan de 2021

por Pascal U E

17 de fev de 2021

por Raja N

13 de jul de 2020

por Neela M

17 de jul de 2020

por Oliver M R

23 de jun de 2020

por Dino H

16 de set de 2021

por Laurent V

16 de jul de 2020

por Yuliia H

28 de jul de 2020

por Julio C

10 de jul de 2020

por Mohamed A G A

15 de set de 2021


2 de jul de 2020

por Zeghraoui M

2 de fev de 2021

por Abrar J

7 de mai de 2020

por Don W

16 de fev de 2020


20 de mar de 2021


17 de ago de 2020

por Shen H

15 de dez de 2020

por Sourav D

28 de mai de 2020