⚙️🤖 Studies in Natural Language Processing (NLP)

◾ Module 1: NLP with Spacy

• Tokenization
• Pos-tagging e Dependencies
• Named Entity Recognition (NER)
• Managing Stopwords
• Creating a Vocabulary
• Searching for similarity
• Expressions Matching
• Displacy Visualization
• Working with Pipelines

◾ Module 2: NLP with NLTK

• Tokenization
• Managing Stopwords and Punctuation
• Stemming
• Metrics
• Costumized Pos-tagging
• NER
• Lemmatization

◾ Module 3: Machine Learning and Deep Learning in NLP

• Implementing Neural Networks (Keras and TensorFlow)
• Spam Classification (NN)
• Creating Embeddings with NNs

◾ Module 4: Sentiment Analysis

• LSTM: Supervised model
• VADER: Regulated model
• Comparison: LSTM x VADER

◾ Module 5: Transformers, BERT and GPTs

• HuggingFace and OpenAI
• Question Answering
• Fill-mask
• Summarization
• Text Generation
• Text Translation

◾ Module 6: Topics Modeling with BERT - BERTopic

• Data Processing
• Main Hyperparameters

◾ Module 7: NLP with Spark

• Working on the Databricks Environment
• Data Pre-processing
• Training and Evaluating the model