Text Mining and Natural Language Processing
( 25 Modules )

Module #1
Introduction to Text Mining
Overview of text mining, importance, and applications

Module #2
Natural Language Processing (NLP) Basics
Foundations of NLP, language models, and text preprocessing

Module #3
Text Preprocessing Techniques
Tokenization, stopword removal, stemming, and lemmatization

Module #4
Text Representation
Bag-of-words, TF-IDF, and vector space models

Module #5
Text Classification
Introduction to text classification, supervised learning, and evaluation metrics

Module #6
Naive Bayes for Text Classification
Application of Naive Bayes to text classification problems

Module #7
Support Vector Machines (SVM) for Text Classification
Using SVM for text classification, kernel tricks, and hyperparameter tuning

Module #8
Deep Learning for Text Classification
Introduction to deep learning, CNN, and RNN for text classification

Module #9
Text Clustering
Introduction to text clustering, k-means, and hierarchical clustering

Module #10
Topic Modeling
Introduction to topic modeling, Latent Dirichlet Allocation (LDA), and Non-Negative Matrix Factorization (NMF)

Module #11
Named Entity Recognition (NER)
Introduction to NER, rule-based and machine learning approaches

Module #12
Part-of-Speech (POS) Tagging
Introduction to POS tagging, rule-based and machine learning approaches

Module #13
Sentiment Analysis
Introduction to sentiment analysis, supervised learning, and deep learning approaches

Module #14
Opinion Mining and Aspect-Based Sentiment Analysis
Extracting opinions and aspect-based sentiment analysis

Module #15
Language Modeling
Introduction to language modeling, n-gram models, and recurrent neural networks (RNNs)

Module #16
Text Generation
Introduction to text generation, language generation, and chatbots

Module #17
Information Extraction
Introduction to information extraction, entity recognition, and relation extraction

Module #18
Question Answering
Introduction to question answering, knowledge graph-based, and deep learning approaches

Module #19
Text Summarization
Introduction to text summarization, extractive and abstractive summarization

Module #20
Specialized Topics in Text Mining
Emotion detection, sarcasm detection, and fake news detection

Module #21
Text Mining in Social Media
Text mining applications in social media, Twitter, and Facebook

Module #22
Text Mining in Healthcare
Text mining applications in healthcare, clinical text analysis, and biomedical text mining

Module #23
Text Mining in Finance
Text mining applications in finance, financial text analysis, and sentiment analysis in finance

Module #24
Practical Applications of Text Mining
Real-world applications, case studies, and project ideas

Module #25
Course Wrap-Up & Conclusion
Planning next steps in Text Mining and Natural Language Processing career

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Text Mining and Natural Language Processing ( 25 Modules )

Text Mining and Natural Language Processing
( 25 Modules )