NLP Certification- BERT, GPTs, HMTL & Multimodal Large Model

NLP Certification- BERT, GPTs, HMTL & Multimodal Large Model

NLP Certification- BERT, GPTs, HMTL & Multimodal Large Model

Multimodal NLP, GPT-4 Expectations, Multilingual NLP for Text Classification, Language Translations & Sentiment Analysis

Language: english

Note: 3.7/5 (67 notes) 10,335 students

Instructor(s): Junaid Zafar

Last update: 2022-06-24

What you’ll learn

  • Understanding of Transformers from scratch to BERT to GPT3
  • Language Translations using Transformers in NLP
  • Text Classification and Implementation of Chatbot in RASA and Spicy
  • GPTs as Few Shot Learners & Multilingual NLP
  • GPT 4- What to expect?
  • 50+ NLP Coding Exercises with Coding Solutions
  • Attention and Multi- Head Attention in NLP Transformers
  • Implement a Transformer for an NLP based task/ activity
  • Google Mum as multilingual unified platfrom



  • Basic Familiarity with the Natural Language Processing is recommended but not essential



This course introduces you to the fundamentals of Transformers in NLP. The topics include are;

1. Recurrent Neural Networks & LSTM

2. Bi-Directional Encoder Representation from Transformers.

3. Masked Language Modelling.

4. Next Sentence Prediction using Transformers.

5. Generative Pre-trained Transformers and their implementation in RASA and SpiCy.

6. Complete Code for Online Fraud Detection System.

7. Complete Code for Text Classification.

8. Complete Code for Language Translation System.

9. Complete Code for Movie Recommender System.

10. Complete Code for Speech to Text Conversion using GPT-2.

11. Complete Code for Chatbot using GPT3.

12. Complete Code for Text Summary System using GPT3.

13. Automated Essay Scoring using Transformer Models.

14. Sentiment Analysis using Pre-trained Transformers.

15. Training and Testing a GPT- 2 for Novel Writing.

16. Game Design using AlphaGo and Transformers.

17. 50+ NLP coding exercises along with complete solutions to complete this certification.

Transformers (formerly known as PyTorch-transformers and pytorch-pretrained-bert) provide thousands of pre-trained models to perform tasks on different modalities such as text, vision, and audio.

These models can be applied on:

  • Text, for tasks like text classification, information extraction, question answering, summarization, translation, text generation, in over 100 languages.

  • Images, for tasks like image classification, object detection, and segmentation.

  • Audio, for tasks like speech recognition and audio classification.

Transformer Models are great with Sequential Data and are Pre-trained which makes them versatile and capable. It allows further to Gain Out-of-the-Box Functionality. Transformer models enable you to take a large-scale LM (language model) trained on a massive amount of text (the complete works of Shakespeare), then update the model for a specific conceptual task, far beyond mere “reading,” such as sentiment analysis and even predictive analysis.


Who this course is for

  • Beginner students interested in learning NLP via Transformers


Course content

  • Introduction
    • NLP Task Dimensions
  • Introduction to NLP Transformers
    • NLP Transformers
  • Why Transformers are preferred over RNNs and LSTMS?
    • Why Transformers?
  • Self Attention in Transformers
    • Self Attention in Transformers
  • What are Large Language Models in NLP
    • Large Language Models in NLP
  • Introduction to Multimodality in NLP
    • Multimodality in NLP- Fundamentals
  • Develop Deep Learning Models for NLP using Python
    • NLP using Deep Learning
  • SuperGlue NLP Benchmarking
    • SuperGlue vs Glue Benchmarking
  • BERT: Why BERT, its detailed architecture and now it works in NLP
    • BERT for Masked Language Modeling and Next Sentence Prediction
  • Google Mum vs Bert
    • Google MUM vs BERT
  • Sentiment Analysis in NLP using NLTK Package
    • Sentiment Analysis using NLTK
  • POLYGLOT Library for multilingual NLP
  • DALL. E Package: Text to Image Generation in NLP
    • Test to Image Generation using DALL. E
  • GPTs as Few Shot Learners
    • GPTs as Multi- Task Learners and why they are of key importance in NLP tasks
  • Multilingual Natural Language Processing
    • Multilingual NLP
  • GPT 4- What to Expect?
    • GPT 4
  • Quantum Language Processing: Extending NLP to Quantum Domain
    • How QNLP works and its implementation using lambeq library
  • Online Fraud Detection System
    • Online Fraud Detection System
  • Text Classification using Transformers
    • Text Classification using Transformers
  • Speech to Text Conversion
    • Speech to Text Conversion
    • BERT
  • Movie Recommender System
    • Movie Recommender System
  • Language Translation in NLP
    • Language Translation in NLP
  • Paraphrasing Text using Transformer
    • Implementation of Pegasus Transformer for Paraphrasing
  • Automated Essay Scoring using Transformers
    • Automated Essay Scoring using Transformers
  • Sentiment Analysis using Transformers
    • Applying Transformers and Aspect-based Sentiment Analysis approaches on Sarcasm
  • Training a GPT- 2 Transformer
    • Training a GPT- 2 Transformer for Harry Porter Novel Writing
  • Generative Pre- Trained Transformers
    • Implementation in RASA and SpiCy
    • Introduction of Text Mining
  • Building a Chess Game using Transformer and AlphaGo
    • Learning Chess by combining AlphaGo and Transformers
  • Building Chatbot with RASA and SpiCy
  • GNNs for NLP
    • NLP using Graph Neural Networks
  • Ethical Consideration of NLP
    • Social and Linguistic Content
  • Open Research Question in NLP
    • Research Gaps in NLP
  • NLP Coding Exercises (50+ NLP Coding Questions with Answers
    • 50+ NLP Coding Exercises with Solutions
  • Textless NLP
    • Zero Code NLP
    • Multimodal NLP
    • Introduction to Software 3.0 Development
    • An Introduction to HMTL
    • Graph Transformer for NLP


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