Data Mining: IntroductionCovers the challenges and opportunities of data mining, practical questions, algorithm components, and applications like shopping basket analysis.
Handling Text: Document Retrieval, Classification, Sentiment AnalysisExplores document retrieval, classification, sentiment analysis, TF-IDF matrices, nearest-neighbor methods, matrix factorization, regularization, LDA, contextualized word vectors, and BERT.
Neural Networks for NLPCovers modern Neural Network approaches to NLP, focusing on word embeddings, Neural Networks for NLP tasks, and future Transfer Learning techniques.
Data Analysis: Text ProcessingCovers text processing techniques for data analysis, including text cleaning, tokenization, stemming, and lemmatization.
