Advanced Techniques in NLP with Machine Learning
( 30 Modules )

Module #1
Introduction to Advanced NLP
Overview of NLP, importance of advanced techniques, and course objectives

Module #2
Deep Learning for NLP
Introduction to deep learning, neural networks, and their applications in NLP

Module #3
Word Embeddings
Word2Vec, GloVe, and other word embedding techniques for representation learning

Module #4
Recurrent Neural Networks (RNNs) for NLP
RNNs, LSTMs, and GRUs for sequence modeling and language modeling

Module #5
Long Short-Term Memory (LSTM) Networks
In-depth exploration of LSTM architectures and their applications in NLP

Module #6
Attention Mechanisms in NLP
Introduction to attention mechanisms and their applications in machine translation, question answering, and text summarization

Module #7
Transformers and BERT
Introduction to transformer architectures, BERT, and their applications in NLP

Module #8
Natural Language Understanding (NLU) with Deep Learning
Using deep learning for NLU tasks such as sentiment analysis, entity recognition, and question answering

Module #9
Natural Language Generation (NLG) with Deep Learning
Using deep learning for NLG tasks such as text generation, machine translation, and chatbots

Module #10
Handling Out-of-Vocabulary (OOV) Words and Words with Multiple Senses
Techniques for handling OOV words and words with multiple senses in NLP

Module #11
Dealing with Noisy and Unbalanced Data in NLP
Techniques for handling noisy and unbalanced data in NLP, including data preprocessing and Oversampling

Module #12
Using Transfer Learning and Pre-Trained Models in NLP
Using pre-trained models and transfer learning for NLP tasks, including fine-tuning and feature extraction

Module #13
Multitask Learning and Multi-Modal Learning in NLP
Using multitask learning and multi-modal learning for NLP tasks, including image and video analysis

Module #14
Graph-Based Methods for NLP
Using graph-based methods for NLP tasks, including graph convolutional networks and graph attention networks

Module #15
Explainable AI (XAI) in NLP
Using techniques for explainable AI in NLP, including feature importance and model interpretability

Module #16
NLP for Low-Resource Languages and Domains
Techniques for NLP in low-resource languages and domains, including transfer learning and few-shot learning

Module #17
Evaluating and Improving NLP Models
Evaluating NLP models using metrics and improving them using techniques such as ensemble methods and hyperparameter tuning

Module #18
Advanced Topics in NLP
Exploring advanced topics in NLP, including multimodal fusion, adversarial training, and lifelong learning

Module #19
NLP for Conversational AI and Dialogue Systems
Using NLP for conversational AI and dialogue systems, including chatbots and voice assistants

Module #20
NLP for Sentiment Analysis and Emotion Detection
Using NLP for sentiment analysis and emotion detection, including aspect-based sentiment analysis

Module #21
NLP for Text Classification and Topic Modeling
Using NLP for text classification and topic modeling, including supervised and unsupervised learning methods

Module #22
NLP for Named Entity Recognition and Information Extraction
Using NLP for named entity recognition and information extraction, including relation extraction and event extraction

Module #23
NLP for Machine Translation and Multilingual NLP
Using NLP for machine translation and multilingual NLP, including sequence-to-sequence models and language models

Module #24
NLP for Question Answering and Dialogue Systems
Using NLP for question answering and dialogue systems, including knowledge graph-based methods

Module #25
NLP for Text Summarization and Generation
Using NLP for text summarization and generation, including extractive and abstractive summarization

Module #26
NLP for Speech Recognition and Synthesis
Using NLP for speech recognition and synthesis, including acoustic models and language models

Module #27
NLP for Multimodal Learning and Fusion
Using NLP for multimodal learning and fusion, including image and video analysis

Module #28
NLP for Adversarial Attacks and Defense
Using NLP for adversarial attacks and defense, including generating and detecting adversarial examples

Module #29
Ethics and Fairness in NLP
Discussing ethics and fairness in NLP, including bias detection and mitigation

Module #30
Course Wrap-Up & Conclusion
Planning next steps in Advanced Techniques in NLP with Machine Learning career

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Advanced Techniques in NLP with Machine Learning ( 30 Modules )

Advanced Techniques in NLP with Machine Learning
( 30 Modules )