1. python techniques for text processing

• Python basics for word processing
• Process text and PDF files
• The most important regular expressions

2. introduction to Natural Language Processing (NLP)

• Concepts of Natural Language Processing
• Use of the SpaCy library for text analysis
• Tokenization, stemming and lemmatization
• Part-of-speech and Named Entity Recognition
• Decomposition of texts with Sentence Segmentation

3. text classification and text analysis

• Introduction to scikit-learn
• Evaluation of classification models with precision, recall and F1 score
• Semantic understanding and sentiment analysis
• Vector-based text representations with Word Vectors
• Sentiment analysis with the NLTK library

4 Topic Modeling and Long Short-Term Memory

• Introduction to topic modeling
• Classification with Latent Dirichlet Allocation (LDA)
• Recognize structures with Non-negative Matrix Factorization (NMF)
• Long Short-Term Memory, GRU and Text Generation
• Implementation of an LSTM for text creation with Keras

5. transformer and attention

• The concept of self-awareness
• Multihead attention and meaning in NLP models
• Encoder and decoder for machine translation and language understanding
• Architectural concepts of common transformer models: GPT-2/3/4, BERT
• Creating a Transformer structure with Python and Keras
• Training and evaluation of a Seq2Seq transformer

6. transfer learning and fine-tuning with Hugging Face

• Introduction to Hugging Face and presentation of pre-trained models
• Selection of suitable models and tokenizers
• Transfer learning and fine-tuning of pre-trained models
• Automatic configuration and customization of models

7th practical project: Training your own chatbot