Natural Language Processing (NLP) Training in Bangkok, Thailand

This training offers a practical and hands-on introduction to Natural Language Processing (NLP) using Python. Participants will learn how machines process and understand human language and how to apply various NLP techniques such as text preprocessing, vectorization, sentiment analysis, named entity recognition, and topic modeling. The course combines foundational theory with real-world projects using tools like NLTK, spaCy, and Hugging Face Transformers.

NLP Basics and Text Preprocessing

Introduction to NLP

• What is NLP and where it's used
• Structured vs unstructured text
• Common applications: chatbots, sentiment analysis, summarization

Core NLP Concepts

• Tokens, corpus, vocabulary, and stopwords
• Lemmatization vs stemming
• Parts of speech and syntactic structures

Text Preprocessing Techniques

• Lowercasing, punctuation removal, and tokenization
• Stopword removal and lemmatization using spaCy and NLTK
• Regex basics for pattern cleaning
• Creating reusable preprocessing functions

Feature Extraction and NLP Models

Feature Engineering in NLP

• Bag of Words model
• TF-IDF (Term Frequency – Inverse Document Frequency)
• N-grams and context windows
• Limitations of sparse representations

Word Embeddings and Vectors

• Introduction to Word2Vec and GloVe
• Vector space and cosine similarity
• Visualizing embeddings with PCA or t-SNE
• Using pre-trained embeddings in spaCy

NLP with Machine Learning Models

• Text classification with Naive Bayes, Logistic Regression
• Building a sentiment analysis model
• Train/test split and evaluation metrics
• Improving accuracy with feature tuning

Named Entity Recognition (NER)

• What is NER and why it matters
• Using spaCy to extract entities
• Custom NER with rule-based and statistical methods
• Visualizing NER outputs

Transformers, Topic Modeling, and Exercises

Introduction to Transformers for NLP

• What are Transformers and how they work
• Overview of Hugging Face Transformers
• Using pre-trained models like BERT for classification
• Fine-tuning vs zero-shot inference

Topic Modeling and Summarization

• Latent Dirichlet Allocation (LDA) for topic modeling
• Preprocessing for LDA
• Using gensim for topic extraction
• Text summarization using Transformers and NLP techniques

Real-World NLP Exercises

Wrap-up and Next Steps

• Review of techniques and tools learned
• Best practices in production NLP workflows
• Additional learning paths and open-source datasets
• Optional: mini-assessment or quiz