This course provides an in-depth exploration of Natural Language Processing (NLP) with a focus on using deep learning techniques to process and analyze human language. Through this course, participants will learn how to apply neural networks, including Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTM), and Transformers, to solve complex NLP tasks such as text classification, machine translation, sentiment analysis, and more.

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Course Objectives:

• Understand the core concepts of Natural Language Processing (NLP)
• Implement deep learning models to solve NLP tasks such as text generation, machine translation, and sentiment analysis
• Work with advanced architectures like Transformers and BERT for state-of-the-art NLP solutions
• Gain hands-on experience with popular deep learning frameworks like TensorFlow or PyTorch for NLP tasks

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Introduction to natural language processing and its importance
Key challenges in NLP: Ambiguity, variability, and context understanding

Tokenization, stemming, lemmatization, and word embeddings
Statistical NLP: N-grams, TF-IDF, and language modeling

Hands-on Lab:
Build a simple text processing pipeline for tokenization and sentiment analysis using Python’s NLTK or SpaCy library

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Module 2: Word Embeddings and Representation 

Vector Representation of Words:

Word2Vec, GloVe, and FastText embeddings

Understanding the mathematics behind word embeddings
Applications of word embeddings in NLP tasks

Introduction to ELMo and BERT
How contextual word embeddings improve performance in NLP tasks

Hands-on Lab:
Implement Word2Vec using Gensim to generate word embeddings and explore nearest neighbor

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Module 3: Sequence Models for NLP – RNN, LSTM, and GRU 

Understanding RNNs and their role in sequential data
Limitations of RNNs: Vanishing and exploding gradients

Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRUs):How LSTMs and GRUs solve the vanishing gradient problem
Applications of LSTM/GRU in NLP tasks such as text classification and sequence generation

Hands-on Lab:
Build and train an LSTM model for text generation using TensorFlow/Kera

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Module 4: Attention Mechanism and Transformers 

The Attention Mechanism:

Introduction to attention and its significance in NLP tasks
How attention helps in aligning source and target in sequence-to-sequence models

Overview of Transformer architecture
Encoder-decoder model and self-attention mechanism
Understanding positional encoding

Applications of Transformers in NLP:
Machine translation, text summarization, and text generation

Hands-on Lab:
Implement a Transformer model using TensorFlow for machine translation

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Module 5: Pretrained Language Models – BERT, GPT, and Beyond

What are pretrained language models and why they are powerful
Overview of BERT, GPT, and T5 architectures
Fine-tuning pretrained models for specific NLP tasks

Understanding the BERT architecture and its applications in NLP
How to use BERT for text classification, sentiment analysis, and question-answering

Generative Pretrained Transformer (GPT) Models:
Overview of GPT, GPT-2, and GPT-3 for text generation and conversational AI

Hands-on Lab:
Fine-tune a BERT model for sentiment classification using Hugging Face’s Transformers library

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How to build and train models for sentiment analysis using LSTM and BERT
Text classification for spam detection, product categorization, etc

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Machine Translation and Text Summarization:

Use sequence-to-sequence models for translating text from one language to another

Abstractive and extractive text summarization

Question Answering Systems:
Building models for answering questions from text using pretrained models like BERT

Hands-on Lab:
Build a text summarization tool using an attention-based Transformer model

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How NLP powers modern conversational agents
Dialogue management and intent recognition

Integrating LSTM and Transformer models into chatbot systems
How to handle multi-turn dialogues and context management

Hands-on Lab:
Build a simple chatbot using GPT and deploy it on a messaging platform

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