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所在平台: Udemy |
课程主页: https://www.udemy.com/course/data-augmentation-in-nlp/
课程评论：没有评论
这门 Coursera 课程名为“NLP 数据增强”（Data Augmentation in NLP），旨在解决机器学习模型在实际应用中面临的数据不足问题。课程强调，尽管拥有最优的机器学习算法，但在真实世界中，训练数据的规模至关重要。许多最先进的机器学习模型都依赖于大规模数据集。 通过本课程，您将学习多种文本数据增强技术。这些技术可以应用于任何自然语言处理（NLP）任务，通过生成额外的训练数据来帮助弥合数据差距，从而快速提升机器学习解决方案的准确性。课程将引导您掌握如何有效地扩充文本数据集，以在真实世界场景中获得更好的模型性能。
You might have optimal machine learning algorithm to solve your problem. But once you apply it in real world soon you will realize that you need to train it on more data. Due to lack of large dataset you will try to further optimize the algorithm, tune hyper-parameters or look for some low tech approach. Most state of the art machine learning models are trained on large datasets. Real world performance of machine learning solutions drastically improves with more data. Through this course you will learn multiple techniques for augmenting text data. These techniques can be used to generate data for any NLP task. This augmented dataset can help you to bridge the gap and quickly improve accuracy of your machine learning solutions.
