Data Augmentation in NLP

Short Description

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

This course includes:

  • 1 hour on-demand video
  • 5 downloadable resources
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of completion

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

Also enroll in : Python & Flask Demonstrations