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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