Text Mining and NLP
( 30 Modules )

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

Module #2
Text Preprocessing
Text cleaning, tokenization, stopword removal, stemming, and lemmatization

Module #3
Text Representation
Bag-of-words, TF-IDF, word embeddings (Word2Vec, GloVe)

Module #4
Text Classification
Introduction to text classification, Naive Bayes, Logistic Regression, SVM

Module #5
Sentiment Analysis
Introduction to sentiment analysis, sentiment classification, and aspect-based sentiment analysis

Module #6
Named Entity Recognition (NER)
Introduction to NER, NER techniques, and popular NER tools

Module #7
Part-of-Speech (POS) Tagging
Introduction to POS tagging, POS tagging techniques, and popular POS taggers

Module #8
Dependency Parsing
Introduction to dependency parsing, dependency parsing techniques, and popular dependency parsers

Module #9
Topic Modeling
Introduction to topic modeling, Latent Dirichlet Allocation (LDA), and Non-Negative Matrix Factorization

Module #10
Text Clustering
Introduction to text clustering, k-means, hierarchical clustering, and DBSCAN

Module #11
Language Modeling
Introduction to language modeling, n-gram models, and recurrent neural networks (RNNs)

Module #12
Deep Learning for NLP
Introduction to deep learning for NLP, word embeddings, and recurrent neural networks (RNNs)

Module #13
Convolutional Neural Networks (CNNs) for NLP
Introduction to CNNs for NLP, text classification, and sentiment analysis

Module #14
Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM) Networks
Introduction to RNNs, LSTMs, and their applications in NLP

Module #15
Transfer Learning in NLP
Introduction to transfer learning, pre-trained language models, and fine-tuning

Module #16
Natural Language Understanding
Introduction to natural language understanding, question answering, and language inference

Module #17
Natural Language Generation
Introduction to natural language generation, text summarization, and chatbots

Module #18
Specialized Topics in NLP
Introduction to specialized topics in NLP, such as speech recognition, machine translation, and dialogue systems

Module #19
Text Mining Tools and Techniques
Introduction to popular text mining tools and techniques, such as NLTK, spaCy, and Gensim

Module #20
Case Studies in Text Mining and NLP
Real-world case studies in text mining and NLP, including sentiment analysis, topic modeling, and language translation

Module #21
Evaluating Text Mining and NLP Models
Evaluation metrics for text mining and NLP models, including precision, recall, F1-score, and ROUGE score

Module #22
Handling Imbalanced Datasets in Text Mining and NLP
Techniques for handling imbalanced datasets in text mining and NLP, including oversampling, undersampling, and SMOTE

Module #23
Dealing with Out-of-Vocabulary (OOV) Words
Techniques for dealing with OOV words, including word embeddings, subword modeling, and tokenization

Module #24
Explainability and Interpretability in NLP
Techniques for explainability and interpretability in NLP, including attention visualization, LIME, and SHAP values

Module #25
Ethics in NLP
Ethical considerations in NLP, including bias, fairness, and transparency

Module #26
NLP for Low-Resource Languages
Challenges and opportunities in NLP for low-resource languages, including language translation, language modeling, and language generation

Module #27
NLP for Multimodal Data
Challenges and opportunities in NLP for multimodal data, including text-image, text-video, and text-audio analysis

Module #28
NLP for Healthcare and Biomedical Texts
Applications of NLP in healthcare and biomedical texts, including clinical text analysis, medical question answering, and biomedical literature mining

Module #29
NLP for Social Media Analysis
Applications of NLP in social media analysis, including sentiment analysis, topic modeling, and social network analysis

Module #30
Course Wrap-Up & Conclusion
Planning next steps in Text Mining and NLP career

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Text Mining and NLP ( 30 Modules )

Text Mining and NLP
( 30 Modules )