Machine Learning for Natural Language Understanding
( 25 Modules )

Module #1
Introduction to NLU
Course overview, importance of NLU, and its applications

Module #2
Mathematical Foundations for NLU
Review of linear algebra, calculus, probability, and statistics

Module #3
Introduction to Machine Learning
Supervised, unsupervised, and reinforcement learning concepts

Module #4
Text Preprocessing for NLU
Tokenization, stemming, lemmatization, and normalization

Module #5
Text Representation Techniques
Bag-of-words, TF-IDF, word embeddings (Word2Vec, GloVe)

Module #6
Language Models
Introduction to language models, n-gram models, and Markov chains

Module #7
Sentiment Analysis
Introduction to sentiment analysis, datasets, and evaluation metrics

Module #8
Sentiment Analysis with Machine Learning
Supervised learning approaches for sentiment analysis

Module #9
Named Entity Recognition
Introduction to NER, datasets, and evaluation metrics

Module #10
Named Entity Recognition with Machine Learning
Supervised learning approaches for NER

Module #11
Part-of-Speech Tagging and Dependency Parsing
Introduction to POS tagging and dependency parsing

Module #12
Part-of-Speech Tagging and Dependency Parsing with Machine Learning
Supervised learning approaches for POS tagging and dependency parsing

Module #13
Introduction to Deep Learning for NLU
Introduction to neural networks, perceptrons, and multilayer perceptrons

Module #14
Convolutional Neural Networks (CNNs) for NLU
Application of CNNs for text classification and sentiment analysis

Module #15
Recurrent Neural Networks (RNNs) for NLU
Introduction to RNNs, LSTMs, and GRUs for sequence modeling

Module #16
Long Short-Term Memory (LSTM) Networks for NLU
Application of LSTMs for language modeling and text classification

Module #17
Transformers for NLU
Introduction to transformers, BERT, and its variants

Module #18
Question Answering and Text Generation
Introduction to question answering and text generation tasks

Module #19
Question Answering with Machine Learning
Supervised learning approaches for question answering

Module #20
Text Generation with Machine Learning
Supervised learning approaches for text generation

Module #21
NLU Applications in Industry
Case studies of NLU applications in customer service, marketing, and healthcare

Module #22
Ethics and Fairness in NLU
Discussion of ethical considerations and fairness in NLU systems

Module #23
Explainability and Interpretability in NLU
Methods for explaining and interpreting NLU models

Module #24
Advanced Topics in NLU
Recent advances and future directions in NLU research

Module #25
Course Wrap-Up & Conclusion
Planning next steps in Machine Learning for Natural Language Understanding career

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Machine Learning for Natural Language Understanding ( 25 Modules )

Machine Learning for Natural Language Understanding
( 25 Modules )