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Вернуться к Machine Learning Data Lifecycle in Production

Отзывы учащихся о курсе Machine Learning Data Lifecycle in Production от партнера

Оценки: 563

О курсе

In the second course of Machine Learning Engineering for Production Specialization, you will build data pipelines by gathering, cleaning, and validating datasets and assessing data quality; implement feature engineering, transformation, and selection with TensorFlow Extended and get the most predictive power out of your data; and establish the data lifecycle by leveraging data lineage and provenance metadata tools and follow data evolution with enterprise data schemas. Understanding machine learning and deep learning concepts is essential, but if you’re looking to build an effective AI career, you need production engineering capabilities as well. Machine learning engineering for production combines the foundational concepts of machine learning with the functional expertise of modern software development and engineering roles to help you develop production-ready skills. Week 1: Collecting, Labeling, and Validating data Week 2: Feature Engineering, Transformation, and Selection Week 3: Data Journey and Data Storage Week 4: Advanced Data Labeling Methods, Data Augmentation, and Preprocessing Different Data Types...

Лучшие рецензии


2 июля 2021 г.

Interesting material. There are quite a lot of typos and many code snippets are directly from the tfx manual pages however the instructions provided and logic of the course is clear.


4 нояб. 2022 г.

After a few hiccups it was actually very good, because you proper had to dive into the documentation of tax to find out the solutions, which is usually the best way to learn it.

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76–100 из 104 отзывов о курсе Machine Learning Data Lifecycle in Production

автор: Hassan K

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28 мая 2021 г.

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9 сент. 2022 г.