Learn next-generation Natural Language Processing with transformers for sentiment analysis, Q&A, similarity search, NER, and more
Table of Contents
What you will learn From this Course:
- Industry standard NLP using transformer models
- Build full-stack question-answering transformer models
- Perform sentiment analysis with transformers models in PyTorch and TensorFlow
- Advanced search technologies like Elasticsearch and Facebook AI Similarity Search (FAISS)
- Create fine-tuned transformers models for specialized use-cases
- Measure performance of language models using advanced metrics like ROUGE
- Vector building techniques like BM25 or dense passage retrievers (DPR)
- An overview of recent developments in NLP
- Understand attention and other key components of transformers
- Learn about key transformers models such as BERT
- Preprocess text data for NLP
- Named entity recognition (NER) using spaCy and transformers
- Fine-tune language classification models
Requirements for this Course:
- Knowledge of Python
- Experience in data science a plus
- Experience in NLP a plus
Description:
Transformer models are the accepted norm in current NLP. They have substantiated themselves as the most expressive, amazing models for language by a huge degree, beating all significant language-based benchmarks consistently.
In this course, we gain proficiency with all you need to know to begin with building state of the art execution NLP applications utilizing transformer models like Google AI’s BERT, or Facebook AI’s DPR.
We cover a few key NLP structures including:
- HuggingFace’s Transformers
- TensorFlow 2
- PyTorch
- spaCy
- NLTK
- Style
What’s more, figure out how to apply transformers to the absolute most famous NLP use-cases:
- Language arrangement/assumption investigation
- Named element acknowledgement (NER)
- Question and Answering
- Likeness/relative learning
All through every one of these utilization cases we work through an assortment of guides to guarantee that what, how, and why transformers are so significant. Close by these areas we additionally work through two full-size NLP projects, one for opinion examination of monetary Reddit information, and another covering a completely fledged open space question-noting application.
The entirety of this is upheld by a few different segments that urge us to figure out how to more readily configure, execute, and measure the exhibition of our models, for example:
- History of NLP and where transformers come from
- Normal preprocessing methods for NLP
- The hypothesis behind transformers
- The most effective method to adjust transformers
Who this course is for:
- Aspiring data scientists and ML engineers interested in NLP
- Practitioners looking to upgrade their skills
- Developers looking to implement NLP solutions
- Data scientist
- Machine Learning Engineer
- Python Developers
Course content:
- Introduction
- NLP and Transformers
- Preprocessing for NLP
- Attention
- Language Classification
- [Project]Sentiment Model With Tensorflow and Transformers
- Long Text Classification With BERT
- Named Entity Recognition (NER)
- Question and Answering
- Metrics For Language
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