Course curriculum
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1
Introduction to NLP
- Course Introduction
- Course Handouts
- What is NLP
- Common tasks in a NLP Project
- NLP Libraries
- Methods of Text Preprocessing - Part 1
- Methods of Text Preprocessing - Part 2
- Methods of Text Preprocessing - Part 3
- Quiz
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2
Building a basic classification model
- Introduction to dataset and problem statement
- Hands on Creating a Basic Review Classification Model
- Understanding TF-IDF and its implementations
- Understanding N-grams
- Methods of Preprocessing
- Hands on Building an basic ANN model
- Limitations of ANN
- Quiz
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3
NLP: Recurrent Neural Network
- Understanding RNN
- Reading - Understanding the Math within RNN
- Backward Propagation in RNN
- Types of RNN
- Hands on Building an RNN Model with Word Indexing
- Word Embeddings
- Hands on Building an RNN Model with Word Embedding
- Advanced RNN Architectures
- Hands on Advanced RNN Structures
- Understanding GRUs
- Hands on BI-directional GRU model
- Understanding Long Short Term Memory Network
- Hands on BI-directional LSTM model
- Quiz
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4
Attention mechanism and Transformers
- Introduction to Seq2Seq Models
- Working of Encoder Decoder in Training and Testing Phase
- Introduction to Problem Statement
- Hands on Encoder Decoder Sequence-to-Sequence Model
- Understanding BLUE, ROUGE and METEOR metric
- Attention Mechanism
- Hands on Encoder Decoder Attention Model
- Introduction to Transformers
- Flow of information in Transformers
- Introduction to Google Colab
- Quiz
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5
Preparing for LLMs
- Origin of Transformers
- Pre trained transformers BERT
- Hands on: Understanding Fine Tuning pretrained Model
- Hands on: Headline extraction using T5 pretrained Transformer Model
- BERT vs GPT
- Quiz
Introduction to Natural Language Processing using PyTorch
This course will help you gain a deep understanding of Natural Language Processing. After this course, you can build advanced NLP Models using the PyTorch framework. With a carefully curated list of resources and exercises, this course is your guide to becoming an NLP expert. By the end of the course, you will have mastered techniques like bag-of-words, RNN, and Transformers.
Who Should Enroll:
Professionals: Individuals looking to expand their skill set and apply NLP across different industries.
Aspiring Students: For those setting out on their journey to mastering text data analysis and making a mark in the tech world.
Key Takeaways from the course
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Learn NLP techniques and build real-world NLP Models.
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Hands-On Experience: Engage with exercises designed to reinforce your learning and apply concepts in real-world scenarios.