This learning path provides a comprehensive introduction to text mining techniques, focusing on practical implementation using Python. It covers essential steps from text preprocessing, such as lower case conversion, punctuation removal, stopword elimination, tokenization, stemming, and lemmatization, to advanced topics like Bag-of-Words, TF-IDF, Part-of-Speech tagging, Word2Vec, Doc2Vec, sentiment analysis, Latent Semantic Analysis, and Latent Dirichlet Allocation. Learners will gain hands-on experience with various text mining concepts and their application in Python.

• Understand the fundamental concepts and applications of text mining.

• Apply various text preprocessing techniques, including lowercasing, punctuation removal, stopword elimination, tokenization, stemming, and lemmatization, using Python.

• Implement and utilize Bag-of-Words and TF-IDF models for text representation.

• Perform Part-of-Speech tagging on text data with NLTK.

• Understand and apply word embedding techniques like Word2Vec and Doc2Vec.

• Conduct sentiment analysis on text data using Python.

• Explore and implement topic modeling techniques such as Latent Semantic Analysis (LSA) and Latent Dirichlet Allocation (LDA).