Chevron Left
Вернуться к AI Workflow: Machine Learning, Visual Recognition and NLP

Отзывы учащихся о курсе AI Workflow: Machine Learning, Visual Recognition and NLP от партнера IBM

4.5
звезд
Оценки: 60
•
Рецензии: 9

О курсе

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....

Лучшие рецензии

NM
6 июля 2020 г.

Dear Team ,\n\nNamaste Everyone !!\n\nExcellent Course structure - ML, VR and NLP.\n\nGreat Learning Module Design by All Faculty.\n\nThanks to everyone!!!

PL
2 мая 2020 г.

The teaching materials are well presented and clear.\n\nJust that the level of this course is a bit not advanced enough.

Фильтр по:

1–9 из 9 отзывов о курсе AI Workflow: Machine Learning, Visual Recognition and NLP

автор: Neela J M

•
7 июля 2020 г.

Dear Team ,

Namaste Everyone !!

Excellent Course structure - ML, VR and NLP.

Great Learning Module Design by All Faculty.

Thanks to everyone!!!

автор: Patrick L

•
3 мая 2020 г.

The teaching materials are well presented and clear.

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

автор: vignaux

•
17 нояб. 2019 г.

Great course with a lot of practice and smart meaning !

автор: Julio C

•
30 июля 2020 г.

Great training !!!

автор: Suryabrata D

•
6 июля 2020 г.

very Informative

автор: BHAVANA g

•
22 сент. 2020 г.

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

автор: Abrar J

•
28 мая 2020 г.

Good for using IBM tools

автор: Lam C V D

•
29 авг. 2020 г.

Theory Overview only

автор: David L

•
26 авг. 2020 г.

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

Начните карьеру или продвиньтесь по карьерной лестнице

Смотреть популярные темы

Популярные курсы и статьи

Получите диплом или сертификат онлайн

Coursera

Сообщество

Еще

Учитесь, где угодно
Загрузить из App StoreЗагрузить в Google Play
Placeholder