Chevron Left
AI Workflow: Machine Learning, Visual Recognition and NLP(으)로 돌아가기

IBM 의 AI Workflow: Machine Learning, Visual Recognition and NLP 학습자 리뷰 및 피드백

4.4
별점
64개의 평가

강좌 소개

This is the fourth course in the IBM AI Enterprise Workflow Certification 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.  Course 4 covers the next stage of the workflow, setting up models and their associated data pipelines for a hypothetical streaming media company.  The first topic covers the complex topic of evaluation metrics, where you will learn best practices for a number of different metrics including regression metrics, classification metrics, and multi-class metrics, which you will use to select the best model for your business challenge.  The next topics cover best practices for different types of models including linear models, tree-based models, and neural networks.  Out-of-the-box Watson models for natural language understanding and visual recognition will be used.  There will be case studies focusing on natural language processing and on image analysis to provide realistic context for the model pipelines.   By the end of this course you will be able to: Discuss common regression, classification, and multilabel classification metrics Explain the use of linear and logistic regression in supervised learning applications Describe common strategies for grid searching and cross-validation Employ evaluation metrics to select models for production use Explain the use of tree-based algorithms in supervised learning applications Explain the use of Neural Networks in supervised learning applications Discuss the major variants of neural networks and recent advances Create a neural net model in Tensorflow Create and test an instance of Watson Visual Recognition Create and test an instance of Watson NLU 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 that you have completed Courses 1 through 3 of the IBM AI Enterprise Workflow specialization and 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....

최상위 리뷰

NM

2020년 7월 6일

Dear Team ,

Namaste Everyone !!

Excellent Course structure - ML, VR and NLP.

Great Learning Module Design by All Faculty.

Thanks to everyone!!!

PL

2020년 5월 2일

The teaching materials are well presented and clear.

Just that the level of this course is a bit not advanced enough.

필터링 기준:

AI Workflow: Machine Learning, Visual Recognition and NLP의 10개 리뷰 중 1~10

교육 기관: Neela M

•

2020년 7월 7일

Dear Team ,

Namaste Everyone !!

Excellent Course structure - ML, VR and NLP.

Great Learning Module Design by All Faculty.

Thanks to everyone!!!

교육 기관: Patrick L

•

2020년 5월 3일

The teaching materials are well presented and clear.

Just that the level of this course is a bit not advanced enough.

교육 기관: vignaux

•

2019년 11월 17일

Great course with a lot of practice and smart meaning !

교육 기관: Julio C

•

2020년 7월 30일

Great training !!!

교육 기관: Suryabrata D

•

2020년 7월 6일

very Informative

교육 기관: Холмухамедова З Б к

•

2022년 5월 30일

Perfect

교육 기관: BHAVANA g

•

2020년 9월 22일

Its pretty difficult to follow up with this course. We must have a good knowledge on Neural n/ws prior starting this course.

교육 기관: Abrar J

•

2020년 5월 28일

Good for using IBM tools

교육 기관: Lam C V D

•

2020년 8월 29일

Theory Overview only

교육 기관: David L

•

2020년 8월 26일

Aspects of this course could be worked on with regards to smoothness, conceptual teaching and grammatical/spelling errors.

Much of the course had confusing terminology/grammatical forms which made multiple lessons difficult to understand. The video quality was, for the most part, very well done -- but some videos moved too quickly to follow (although it may just be my current level is too low).

I really enjoyed the case studies for the most part; they were challenging and informative, forcing you to learn yourself. There were a couple of areas where I would've appreciated more guidance, such as setting up the MLP/CNN at the end of Week 2. I had no idea that we needed to use a sparse-categorical-crossentropy loss function until I looked at the solution -- and I'm not sure other students would know the same.

Otherwise, it was a useful course.

Coursera Footer

경력을 시작하거나 쌓기

인기 있는 주제 찾아보기

인기 강좌 및 문서

온라인으로 학위 또는 자격증 취득

Coursera

커뮤니티

기타

어디에서나 학습
앱스토어에서 다운로드하기구글 플레이에서 받기
Placeholder