Module #1 Introduction to NLP and ML Overview of Natural Language Processing and Machine Learning, importance of NLP in AI, and the role of ML in NLP.
Module #2 NLP Fundamentals Basic concepts of NLP, including tokenization, stemming, lemmatization, and named entity recognition.
Module #3 Text Preprocessing Techniques for preprocessing text data, including handling missing values, removing stop words, and feature scaling.
Module #4 Text Representation Methods for representing text data, including bag-of-words, TF-IDF, and word embeddings.
Module #5 Supervised Learning in NLP Introduction to supervised learning for NLP tasks, including text classification and sentiment analysis.
Module #6 Text Classification Techniques for text classification, including logistic regression, decision trees, and random forests.
Module #7 Sentiment Analysis Methods for sentiment analysis, including machine learning and deep learning approaches.
Module #8 Named Entity Recognition Techniques for named entity recognition, including rule-based and machine learning approaches.
Module #9 Part-of-Speech Tagging Methods for part-of-speech tagging, including hidden Markov models and conditional random fields.
Module #10 Dependency Parsing Techniques for dependency parsing, including transition-based and graph-based approaches.
Module #11 Language Modeling Introduction to language modeling, including n-gram models and recurrent neural networks.
Module #12 Word Embeddings Methods for learning word embeddings, including Word2Vec and GloVe.
Module #13 Recurrent Neural Networks for NLP Introduction to RNNs for NLP tasks, including text classification and language modeling.
Module #14 Long Short-Term Memory Networks Methods for using LSTM networks for NLP tasks, including sentiment analysis and machine translation.
Module #15 Convolutional Neural Networks for NLP Introduction to CNNs for NLP tasks, including text classification and sentiment analysis.
Module #16 Transfer Learning in NLP Methods for using pre-trained models and fine-tuning for NLP tasks, including language modeling and text classification.
Module #17 Attention Mechanisms in NLP Introduction to attention mechanisms for NLP tasks, including machine translation and question answering.
Module #18 Transformer Models for NLP Methods for using transformer models for NLP tasks, including language modeling and text classification.
Module #19 Natural Language Generation Introduction to natural language generation, including text summarization and chatbots.
Module #20 Question Answering Methods for question answering, including machine learning and deep learning approaches.
Module #21 Machine Translation Introduction to machine translation, including rule-based and statistical approaches.
Module #22 Evaluating NLP Models Methods for evaluating NLP models, including metrics and evaluation protocols.
Module #23 NLP for Specialized Domains Introduction to NLP for specialized domains, including healthcare, finance, and law.
Module #24 Ethical Considerations in NLP Ethical considerations for NLP, including bias, fairness, and transparency.
Module #25 Course Wrap-Up & Conclusion Planning next steps in Machine Learning for Natural Language Processing career
