Machine Learning for Natural Language Processing
( 25 Modules )

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

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Machine Learning for Natural Language Processing ( 25 Modules )

Machine Learning for Natural Language Processing
( 25 Modules )