Module #1 Introduction to NLP Overview of Natural Language Processing and its applications
Module #2 Deep Learning Fundamentals Review of deep learning concepts, including neural networks, activation functions, and backpropagation
Module #3 NLP Task Types Classification of NLP tasks, including language modeling, text classification, and sequence-to-sequence tasks
Module #4 Text Preprocessing Text preprocessing techniques, including tokenization, stemming, and lemmatization
Module #5 Word Embeddings Introduction to word embeddings, including Word2Vec and GloVe
Module #6 Language Modeling Introduction to language modeling, including statistical language models and neural language models
Module #7 Recurrent Neural Networks (RNNs) Introduction to RNNs, including simple RNNs, LSTM, and GRU
Module #8 Long Short-Term Memory (LSTM) Networks In-depth exploration of LSTM networks
Module #9 Gated Recurrent Units (GRU) In-depth exploration of GRU networks
Module #10 Text Classification Introduction to text classification, including binary and multi-class classification
Module #11 Convolutional Neural Networks (CNNs) for NLP Introduction to CNNs for NLP, including text classification and sentiment analysis
Module #12 Recurrent Convolutional Neural Networks (RCNNs) Introduction to RCNNs, including text classification and language modeling
Module #13 Attention Mechanism Introduction to attention mechanism, including self-attention and cross-attention
Module #14 Transformers In-depth exploration of transformers, including BERT and RoBERTa
Module #15 Sequence-to-Sequence Tasks Introduction to sequence-to-sequence tasks, including machine translation and text generation
Module #16 Named Entity Recognition (NER) Introduction to NER, including named entity recognition and entity disambiguation
Module #17 Part-of-Speech (POS) Tagging Introduction to POS tagging, including rule-based and machine learning approaches
Module #18 Dependency Parsing Introduction to dependency parsing, including constituency parsing and dependency grammar
Module #19 Sentiment Analysis Introduction to sentiment analysis, including binary and multi-class sentiment analysis
Module #20 Question Answering Introduction to question answering, including extractive and abstractive question answering
Module #21 Language Translation Introduction to language translation, including statistical and neural machine translation
Module #22 Text Generation Introduction to text generation, including language models and sequence-to-sequence models
Module #23 Deep Learning Architectures for NLP Exploration of advanced deep learning architectures for NLP, including graph neural networks and capsule networks
Module #24 NLP with PyTorch Hands-on implementation of NLP tasks using PyTorch
Module #25 Course Wrap-Up & Conclusion Planning next steps in Deep Learning for Natural Language Processing career
