Master Natural Language Processing With Transformers

NLP with Transformers | GenAI | Hugging Face | Deep Learning

What you'll learn

Fundamental concepts and applications of Natural Language Processing (NLP)

Learn what transformers are and how they revolutionized NLP tasks.

Setting up a Python environment and working with VSCode for NLP projects

Installing and using essential NLP libraries, such as NLTK, Hugging face, Pytroch

Gain practical skills in fine-tuning pre-trained models on specific datasets for improved performance.

Lean about Hugging Face transformer, dataset and tokenization libraries

Explain Self-Attention, Multi-head Attention, Position encoding, encoder and decoder architecture

Key text preprocessing techniques, including tokenization, stemming, lemmatization, stop words, and spelling correction, with practical coding examples

Various text representation methods, including Bag of Words, n-grams, one-hot encoding, and TF-IDF

An introduction to Word2Vec, along with practical implementations of CBOW and skip-gram models, and the use of pre-trained Word2Vec models

Comprehensive understanding of transformer architectures.

Detailed study of the BERT model and its application in sentiment classification, along with hands-on projects using Hugging Face libraries

Fine-tune language classification models with BERT

Overview and practical project involving the T5 model for text translation

Fine-tuning Text translation model with T5

Development of hands-on coding skills through practical projects and exercises

An understanding of modern NLP tools and techniques used in the industry for building robust NLP applications.

Requirements

Strong knowledge of Python programming

Basic understanding of machine learning concepts, such as model training, evaluation, and supervised learning.

Familiarity with deep learning frameworks, especially PyTorch.

Description

Unlock the power of modern Natural Language Processing (NLP) and elevate your skills with this comprehensive course on NLP with a focus on Transformers. This course will guide you through the essentials of Transformer models, from understanding the attention mechanism to leveraging pre-trained models.  If so, then this course is for you what you need! We have divided this course into Chapters. In each chapter, you will be learning a new concept for Natural Language Processing with Transformers. These are some of the topics that we will be covering in this course:Starting from an introduction to NLP and setting up your Python environment, you'll gain hands-on experience with text preprocessing methods, including tokenization, stemming, lemmatization, and handling special characters. You will learn how to represent text data effectively through Bag of Words, n-grams, and TF-IDF, and explore the groundbreaking Word2Vec model with practical coding exercises.Dive deep into the workings of transformers, including self-attention, multi-head attention, and the role of position encoding. Understand the architecture of transformer encoders and decoders and learn how to train and use these powerful models for real-world applications.The course features projects using state-of-the-art pre-trained models from Hugging Face, such as BERT for sentiment analysis and T5 for text translation. With guided coding exercises and step-by-step project walkthroughs, you’ll solidify your understanding and build your confidence in applying these models to complex NLP tasks.By the end of this course, you’ll be equipped with practical skills to tackle NLP challenges, build robust solutions, and advance your career in data science or machine learning. If you’re ready to master NLP with modern tools and hands-on projects, this course is perfect for you.What You’ll Learn:- Comprehensive text preprocessing techniques with real coding examples- Text representation methods including Bag of Words, TF-IDF, and Word2Vec- In-depth understanding of transformer architecture and attention mechanisms- How to implement and use BERT for sentiment classification- How to build a text translation project using the T5 model- Practical experience with the Hugging Face ecosystemWho This Course Is For:- Intermediate to advanced NLP learners- Machine learning engineers and data scientists- Python developers interested in NLP applications- AI enthusiasts and researchersEmbark on this journey to mastering NLP with Transformers and build your expertise with hands-on projects and state-of-the-art tools.Feel Free to message me on the Udemy Ques and Ans board, if you have any queries about this Course. We'll give you the best reply as soon as possible.Thanks for checking the course Page, and I hope to see you in my course.

Overview

Section 1: Introduction

Lecture 1 Introduction to Course

Lecture 2 Introduction to NLP

Lecture 3 Setting Up the Python Environment

Lecture 4 Installing and Configuring VSCode

Lecture 5 Installing Essential NLP Packages

Section 2: Text Preprocessing

Lecture 6 What is Lower Casing and and Implementation

Lecture 7 Tokenization: Concepts and Coding

Lecture 8 Handling Punctuation: Techniques and Code Examples

Lecture 9 Processing Chat Words with Coding Examples

Lecture 10 Handling Emojis: Strategies and Code Implementation

Lecture 11 Stemming: Concepts and Coding

Lecture 12 Lemmatization: Concepts and Coding

Lecture 13 What is Stop Words and Coding

Lecture 14 Spelling Correction and Coding

Section 3: Text Representation

Lecture 15 Bag of Words

Lecture 16 n-grams

Lecture 17 One Hot Encoding

Lecture 18 Tf-Idf

Lecture 19 word2vec Introduction

Lecture 20 word2vec CBOW

Lecture 21 word2vec CBOW Coding

Lecture 22 Word2vec Skip gram

Lecture 23 Pre-Trained Word2Vec Model

Section 4: Transformers

Lecture 24 Introduction to Transformers

Lecture 25 Understanding Self-Attention Mechanism

Lecture 26 Multi-Head Attention Explained

Lecture 27 Position Encoding: Concept and Importance

Lecture 28 Transformer Encoder Architecture

Lecture 29 Transformer Decoder Part 1

Lecture 30 Transformer Decoder Part 2

Section 5: BERT Model - Sentiment Classification

Lecture 31 Introduction to Hugging Face Ecosystem

Lecture 32 Overview of the BERT Model Architecture

Lecture 33 Project: Building a Sentiment Classification Model Using BERT

Section 6: T5 - Text Translation

Lecture 34 Overview of the T5 Model and Its Capabilities

Lecture 35 Project: Training a Text Translation Model Using T5

Section 7: Conclusion

Lecture 36 Thank you

NLP Enthusiasts and Researchers,Data Scientists and Machine Learning Practitioners,Intermediate to Advanced Learners in NLP,NLP Enthusiasts,Data scientists looking to expand their NLP knowledge,Students or professionals pursuing a career in NLP or AI.,Machine learning engineers interested in transformers and pre-trained models,AI enthusiasts eager to learn about the Hugging Face ecosystem