Data Augmentation in NLP

Data Augmentation in NLP, available at $27.99, has an average rating of 4.55, with 12 lectures, based on 130 reviews, and has 24543 subscribers.

You will learn about 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 is ideal for individuals who are Anyone interested in machine learning and NLP. It is particularly useful for Anyone interested in machine learning and NLP.

Enroll now: Data Augmentation in NLP

Summary

Title: Data Augmentation in NLP

Price: $27.99

Average Rating: 4.55

Number of Lectures: 12

Number of Published Lectures: 12

Number of Curriculum Items: 12

Number of Published Curriculum Objects: 12

Original Price: $19.99

Quality Status: approved

Status: Live

What You Will 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

Who Should Attend

Anyone interested in machine learning and NLP.

Target Audiences

Anyone interested in machine learning and NLP.

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.

Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction

Chapter 2: Word embeddings

Lecture 1: Data augmentation using word embeddings

Lecture 2: Implementation

Chapter 3: BERT

Lecture 1: Data augmentation using BERT

Lecture 2: Implementation

Chapter 4: Back Translation

Lecture 1: Data augmentation using Back Translation

Lecture 2: Implementation

Chapter 5: T5

Lecture 1: Data augmentation using T5

Lecture 2: Implementation

Chapter 6: Ensemble Approach

Lecture 1: Similarity filter

Lecture 2: Ensemble Approach

Chapter 7: Comparison

Lecture 1: Comparison

Instructors

Prathamesh Dahale
Computer Science Engineer | Specialist Programmer

Rating Distribution

1 stars: 2 votes

2 stars: 3 votes

3 stars: 18 votes

4 stars: 44 votes

5 stars: 63 votes

Frequently Asked Questions

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You can view and review the lecture materials indefinitely, like an on-demand channel.

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