Course Details
Advanced Natural Language Processing (NLP)
Data Science
Created On
September 15, 2023Description
Advanced Natural Language Processing (NLP) is an in-depth study of advanced techniques and technologies used in understanding, generating, and manipulating human language using artificial intelligence. This course delves into state-of-the-art NLP models, neural networks, and applications, equipping students with advanced skills to tackle complex language-related challenges.
Overview
This advanced NLP course syllabus covers cutting-edge topics and techniques in the field, preparing students for advanced applications and research in natural language processing. Students will gain advanced **NLP expertise, enhancing career prospects, problem-solving skills, ethical awareness, hands-on experience, and networking opportunities**.
Features
- In-Depth NLP Fundamentals
- Neural Network Expertise including RNNs, LSTMs, attention mechanisms, and transformer models
- Cutting-Edge Language Models BERT, GPT, and advanced text generation techniques
- Advanced NLP Applications
- Ethical NLP and Fairness
- NLP Research Insights
- Capstone Project
- Quizzes, Assignments & Resources
- Hands-on practicals
- Completion certificate
What you'll learn
- Cutting-Edge NLP Techniques
- Multimodal NLP
- Advanced Applications
- Ethical NLP
- Research and Innovation
Prerequisites
Curriculum
- 11 modules
1.1 Review of NLP Basics
Key concepts and techniques from introductory NLP
1.2 Advanced Text Preprocessing
Named entity recognition refinement
Coreference resolution
Handling noisy and unstructured text data
2.1 Introduction to Neural Networks
Deep learning fundamentals
Building blocks of neural networks
2.2 Recurrent Neural Networks (RNNs)
Understanding RNNs
Applications in sequence modeling
2.3 Long Short-Term Memory (LSTM) Networks
Solving vanishing gradient problem
NLP tasks with LSTMs
2.4 Attention Mechanisms
Attention and its significance
Transformer architecture
3.1 Transfer Learning in NLP
Pre-trained language models
Fine-tuning for specific tasks
3.2 BERT and Transformer Models
Understanding BERT architecture
Fine-tuning BERT for various NLP tasks
3.3 GPT (Generative Pre-trained Transformer) Models
Overview of GPT variants
Text generation and completion with GPT
4.1 Seq2Seq and Encoder-Decoder Architecture
Introduction to sequence-to-sequence models
Applications in machine translation, summarization, and chatbots
4.2 Attention-Based Seq2Seq Models
Attention mechanisms in sequence-to-sequence
Advanced applications in NLP
4.3 Transformer-Based Seq2Seq Models
Transformer architecture for sequence-to-sequence tasks
State-of-the-art models and applications
5.1 Text Summarization
Extractive vs. abstractive summarization
Building a text summarization model
5.2 Storytelling and Creative Text Generation
Generating narratives and stories
Controlling creativity in text generation
6.1 Multilingual NLP
Challenges in multilingual NLP
Cross-lingual embeddings and models
6.2 Zero-Shot and Few-Shot Learning for Languages
Learning languages with minimal training data
Applications in low-resource settings
7.1 Question Answering Systems
Building QA models
SQuAD and other QA datasets
7.2 Information Extraction
Named entity recognition
Relation extraction
7.3 Advanced Sentiment Analysis
Aspect-based sentiment analysis
Fine-grained sentiment analysis
8.1 Addressing Bias in Pre-trained Models
Debiasing techniques
Ethical considerations in NLP
8.2 Fairness and Interpretability
Fairness evaluation metrics
Interpretable NLP models
9.1 Recent Advances in NLP
State-of-the-art models and breakthroughs
NLP research conferences and publications
9.2 Future Directions
Emerging NLP applications
Challenges and opportunities
10.1 Project Proposal
Selecting an advanced NLP project
Defining project objectives and scope
10.2 Project Development
Data collection and preprocessing
Model design and training
10.3 Project Presentation and Documentation
Presenting project findings
Documenting the project for future reference
11.1 Course Recap and Key Takeaways
Review of advanced NLP concepts
Skills acquired during the course
11.2 Career Opportunities in Advanced NLP
Roles in industry and research
Continuing education and research opportunities
Instructors
Skoliko Faculty
₹5500.00
- 11 Modules
- 15 Hours
- Data Science
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