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Data Visualization and Text Mining course

This repository contains the notebooks shown during the lessons of Data Visualization and Text Mining course.

Course contents:

  • NLP in practice: use SpaCy and NLTK to understand NLP algorithms
  • Text Classification with ML: apply Machine Learning to text data sources
    • Supervised Learning in Machine Learning
    • Bag-of-Words
    • CountVectorizer vs TDFVectorizer
    • Text Classification project
  • Topic Modeling: identify topics in a unsupervised dataset
    • Latent Dirischlet Allocation
    • Non Negative Matrix Factorization
  • Data Visualization: see how to visualize your dataset in Python
  • DashBoards: make Plots dynamic building an interactive dashboard, using Dash
  • Neural Networks: how to classify data with Neural Networks - Tensorflow and PyTorch samples
  • Embeddings: moving to a wector-based representation for each word
  • LSTM: long-short term memory neural network for text generation and classification
  • Transformers: Transformers architecture
  • BERT: use a BERT model for text classification and QA
  • LLM: discover how generative models work for NLP use cases

Updated: Academic Year 2024/2025