Data Augmentation in NLP
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English + .srt | Duration: 12 lectures (51m) | Size: 269.4 MB
Augment your Dataset and Outperform What you'll learn:

Data Augmentation using Word Embeddings
Data Augmentation using Word Embeddings - Implementation
Data Augmentation using BERT
Data Augmentation using BERT - Implementation
Data Augmentation using Back Translation
Data Augmentation using Back Translation - Implementation
Data Augmentation using T5
Data Augmentation using T5 - Implementation
Improving Quality of Augmented Data using Similarity Filter
Ensemble Approach for Data Augmentation
Comparison of Data Augmentation Techniques

Requirements
Basic knowledge of machine learning and NLP is good to have

Description
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.

Who this course is for
Anyone interested in machine learning and NLP

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