| Week 1 |
Lecture 1: Introduction to NLP and AI
- Course Overview
- Historical Evolution of AI and NLP
- Key Concepts in Machine Learning and NLP
- Overview of NLP Applications in Various Domains
- Basic Text Processing and Language Understanding
- Challenges and Limitations in NLP
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| Week 2 |
Lecture 2: Text Cleaning and Preprocessing
- Text Cleaning and Preprocessing
- Techniques for Noise Reduction
- Text Normalization and Tokenization
- Lemmatization and Stemming
- Co-occurrence Matrix in Text Analysis
- Feature Extraction from Text
- Regular Expressions in Text Processing
- Basic Overview of Word Embeddings
- Overview of python libraries such as NLTK, Spacy, regex etc.
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| Week 3 |
Lecture 3: Fundamentals of Machine Learning for NLP
- Supervised Learning and Unsupervised Learning - Key Algorithms for NLP
- Evaluation Metrics for NLP Models
- Training, Validation, and Test Sets in Model Development
- Overfitting and Underfitting in NLP
- Cross-Validation Techniques
- Introduction to Python ML Libraries
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| Week 4 |
Lecture 4: Neural Networks and Deep Learning in NLP
- Neural Networks and Deep Learning in NLP
- Activation Functions and Network Topologies
- Backpropagation and Gradient Descent
- CNNs and RNNs for NLP
- Advanced RNNs: LSTM and GRU
- Sequence Modeling in NLP
- Challenges in Deep Learning for NLP
- Case Studies in Deep Learning for NLP
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| Week 5 |
Lecture 5: Transformer Models and Attention Mechanisms
- Transformer Models and Attention Mechanisms
- Understanding the Transformer Architecture
- Concepts of Self-Attention and Positional Encoding
- Overview of BERT, GPT, and Transformer Variants
- Applications of Transformer Models in NLP
- Training Transformer Models for NLP Tasks
- Challenges and Solutions with Transformer Models
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| Week 6 |
Lecture 6: Syntax, Parsing, Word Embeddings, and POS Tagging
- Syntax, Parsing, Word Embeddings, and POS Tagging
- Syntax in Natural Language Processing
- Dependency and Constituency Parsing
- Deep Dive into Word Embeddings
- Word2Vec, GloVe, and FastText Embeddings
- Using Embeddings in NLP Tasks
- Part-of-Speech (POS) Tagging: Importance, Methods, and Tools
- Practical Parsing, Embedding, and POS Tagging Techniques
- Vector Database
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| Week 7 |
Lecture 7: Semantic Analysis, Language Models, and Question Answering
- Semantic Analysis, Language Models, and Question Answering
- Semantic Role Labeling (SRL)
- Knowledge Graphs in Semantic Analysis
- Advances in Contextual Embeddings
- Overview of Language Models in NLP
- Introduction to Question Answering System and Utilizing Language Models
- Challenges in QA and Semantic Analysis
- Word Sense Disambiguation Techniques
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| Week 8 |
Lecture 8: Text Classification and Machine Translation + Midterm Exam
- MidTerm Exams!!
- Text Classification and Machine Translation
- Fundamentals of Text Classification
- Techniques and Algorithms for Classification
- Introduction to Machine Translation
- Neural Machine Translation (NMT) models
- Challenges and Evaluation Metrics for MT
- Practical Implementation of MT Systems
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| Week 9 |
Lecture 9: Advanced Topics in Machine Learning and NLP
- Advanced Topics in Machine Learning and NLP
- Deep Transfer Learning in NLP
- Strategies for Addressing Data Imbalance
- Model Interpretability and Explainability in NLP
- Advanced Optimization Techniques in NLP
- Utilizing Pre-Trained NLP Models
- Case Studies of Advanced ML in NLP
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| Week 10 |
Lecture 10: Named Entity Recognition, Information Retrieval and Search
- Named Entity Recognition, Information Retrieval and Search
- Advanced Techniques in NER
- Contextual NER and Its Applications
- Fundamentals of Information Retrieval
- Deep Dive into TF-IDF and Co-occurrence Matrix
- Search Engines and Indexing Techniques
- Evaluation Metrics in Information Retrieval
- Case Studies and Real-World Applications
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| Week 11 |
Lecture 11: Advanced Machine Translation and Summarization
- Advanced Machine Translation and Summarization
- Advanced Techniques in Machine Translation
- Handling Low-Resource Languages in MT
- Text Summarization
- Extractive vs. Abstractive Summarization
- Challenges in Summarization
- Evaluation of Summarization Techniques
- Current Trends in MT and Summarization
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| Week 12 |
Lecture 12: Speech Processing and Conversational AI
- Speech Processing and Conversational AI
- Basics of Speech Recognition
- Challenges in Automatic Speech Recognition (ASR)
- Design and Development of Conversational Agents
- Evaluating Dialogue Systems in Conversational AI
- Multimodal Interaction in Conversational AI
- Natural Language Understanding in Conversational AI
- Case Studies in Speech Processing and Conversational AI
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| Week 13 |
Lecture 13: Generative AI, Products, Techniques, APIs, and Ethical Considerations
- Generative AI, Products, Techniques, APIs, and Ethical Considerations
- Generative Models in NLP (ChatGPT, BARD, Gemini, Llama)
- Advanced Prompt Engineering: Few-shot, Chain-of-thought, and Self-Consistency
- Expanding LLM Capabilities: Knowledge Generation Prompting and Program-aided (PAL)
- Developing an API with MongoDB and ngrok, and interfacing via Postman
- LLM Safety: Prompt Injection, Prompt Leaking, Jailbreaking
- Responsible AI, Data Privacy, Ethical Considerations, and Governance in AI
- Business Intelligence, Marketing, Analytics, and Brand Analysis Use Cases
- Introduction to Product Management and Business Considerations
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| Week 14 |
Lecture 14: Training and Fine-Tuning LLMs, Hugging Face, LangChain, and RAG
- Training and Fine-Tuning LLMs, Hugging Face, LangChain, and RAG
- RAG: Retrieval Augmented Generation
- Training of Chat GPT with Reinforcement Learning, HITL, and Proximal Policy Optimization
- Evaluation of LLMs: MMLU, HellaSwag Benchmark, ARC, WinoGrade, GSM-8k, Truthful QA, PIQA
- LoRA and QLoRA for Efficient Model Adaptation
- Fine Tuning LLMs, Best Practices and Challenges
- Fine-Tuned LLMs Application
- Overview and Practical Use of Hugging Face in NLP Based Products
- Utilizing LangChain and Llama for Customized Language Models
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| Week 15 |
Lecture 15: Course Review, Future Trends, and Project Presentations + Final Exam
- Course Review, Future Trends, and Project Presentations + Final Exam
- Recap of Key NLP and Generative AI Concepts
- Discussion on the Future Trends in NLP and Generative AI
- Student Project Presentations
- Feedback and Review of Projects
- Resources for Advanced Learning
- Closing Remarks and Course Evaluation
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