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Вернуться к AI Workflow: Business Priorities and Data Ingestion

Отзывы учащихся о курсе AI Workflow: Business Priorities and Data Ingestion от партнера IBM

4.2
звезд
Оценки: 105
Рецензии: 25

О курсе

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 из 25 отзывов о курсе AI Workflow: Business Priorities and Data Ingestion

автор: Yifan Z

16 февр. 2020 г.

For all of these courses, no data source has been provided 100%. All of them have an issue and can not be used for the case study. Also for the second module, even no solution has been provided in the case-study-solution notebook. To be honest, the lectures didn't provide us enough material to deal with the course and I totally learn nothing from this course. It just wastes our time.

автор: Tracy P

22 февр. 2020 г.

Great course; would be better if the case study file was not broken (missing files, missing table in db, etc.)

автор: Luis L

10 янв. 2020 г.

Basic introduction to data ingestion pipeline.

автор: Jonathan V

22 мая 2020 г.

The instructor has completely failed to create a course that works and does not adequately answer questions. There were so many errors in the week 2 data ingest notebook that even after I fixed things, there remained errors at the end that made it impossible to use and the instructor was never able to fix it (the key error since invoive_item_id doesn't exist). I learned nothing from this awful course.

автор: Armen M

11 апр. 2020 г.

Country table missing in lab db.The lab was failed.To Sad.

It was just waste a time.

автор: Nagendra P P

21 авг. 2020 г.

I am totally amazed with the course content. I have never such a well structured course like this. Key take aways. are: Every Video is short and crisp, and each video is followed by transcript with hyperlinks. If one watches the video, the transcript gives view point and hyperlinks really makes you ready for the quiz which is the following section. Every problem in the course, gives exposure to various methodologies and reason for using them. Finally, the notebooks which were shared both local version and Watson version. This in turn giving you liberty to use Cloud platform to get hands on. Great content, thank you

автор: ELINGUI P U

5 сент. 2020 г.

I love the practical business focus of your IBM! Keep doing great stuff

автор: B R N

13 июля 2020 г.

Brilliant Instructors and well Structured Course

автор: Neela M

17 июля 2020 г.

Excellent Course with Practical Case STUDY.

автор: Oliver M R

23 июня 2020 г.

Excelente curso, nos muestra lo esencial

автор: Laurent V

16 июля 2020 г.

very efficient way of learning

автор: Yuliia H

28 июля 2020 г.

Great! Like it so much!

автор: Julio C

10 июля 2020 г.

Excellent training !!!

автор: PARITOSH P

2 июля 2020 г.

Very good course.

автор: Abrar J

7 мая 2020 г.

Good

автор: Don W

16 февр. 2020 г.

The course goes over practical considerations relevant to applying data science in the real world, but the final case study focuses more on data ingestion. It would have been nice if there was some component dedicated to practicing the 'empathize' stage and gaining business problem awareness.

автор: BHAVANA g

17 авг. 2020 г.

Really nice... I have never automated the process of loading data..

This is new and business oriented when compared to other courses.

Although, prior knowledge of playing around with ml is required.

автор: Sourav K D

28 мая 2020 г.

This is a great course .Lectures and materials are excellent.case study is not organised properly.

автор: khalil e

8 июля 2020 г.

very interesting to learn good practices for data digestion

автор: Mahjube C

13 мая 2020 г.

The Data Ingestion notebook was such a great experience.

автор: Erwin F

1 июля 2020 г.

Some of the answers did not work. Location of files not consistent with code. Some bits very detailed.

Overtesting

автор: lorenzo s

8 окт. 2020 г.

Fine, even if a good knowledge of python and related packages is required

автор: Alberto P

9 нояб. 2020 г.

Not so interesting , a little slow

автор: Matthew D

7 июля 2020 г.

I decided not to stick with it. It just wasn't coherently put together.

автор: Lam C V D

29 авг. 2020 г.

Grader problem unfixed by IBM