Natural Language Processing: Text Mining and Analytics Proficiency (Advanced Level)

Topics covered

• build and evaluate simple NLP models
• compare challenges and deployment strategies for NLP projects across various industries
• demonstrate additional features of TextBlob including sentiment analysis, classification models, tokenization, word/phrase frequencies, word inflection, and spelling correction
• demonstrate advanced Genism features such as identifying query similarity
• demonstrate building an LDA model for topic modeling using genism
• demonstrate building bigram and trigram for topic modeling using Genism
• demonstrate feature engineering on data
• demonstrate installation and topic modeling with Gensim
• demonstrate multi-language part of speech tagging and morphological analysis using PolyGlot
• demonstrate multi-language parts of speech tagging using polyglot including language detection, sentiment analysis, and transliteration
• demonstrate simple model building and evaluation using the Decision Tree classifier, logistic regression, and SVM
• demonstrate simple model building and evaluation using the Random Forest Classifier, Naïve Bayes, and KNN and compare the results of all the models
• demonstrate the use of a word cloud and sentiment distribution
• deploy AutoML, PyCaret, and Streamlit models
• identify best practices for NLP projects across various industries
• install linguistic features including noun phrase extraction, POS, parsing, and WordNet integration
• interpret the tuning of models for better results and outline their evaluation using different search methods