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Master LLM: Large Language Models with Transformers
77 students
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.
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Course content
7 sections • 36 lectures • 5h 6m total length
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 ecosystem
Who This Course Is For:
- Intermediate to advanced NLP learners
- Machine learning engineers and data scientists
- Python developers interested in NLP applications
- AI enthusiasts and researchers
Embark on this journey to mastering NLP with Transformers and build your expertise with hands-on projects and state-of-the-art tools.
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Who this course is for:
- 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