AI for Natural Language Processing is an advanced, one-day course. It helps data and tech professionals unlock the potential of text data through machine learning. Participants learn how to apply text data to machine learning problems. They gain hands-on experience building natural language models with state-of-the-art tools such as SpaCy, SentencePiece, and Transformers. By the end, participants can build AI solutions using text data, opening new possibilities for AI to deliver value to their organization.
NLP Preliminaries opens the day with core text-processing concepts, tokenization, and the vocabulary needed for later modules. Data Preparation then has participants clean, normalize, and tokenize raw text using SpaCy and SentencePiece, building the pipelines that feed every downstream model. Traditional ML for NLP applies classical algorithms such as bag-of-words and TF-IDF-based classifiers to text tasks, establishing a baseline. Neural Networks for NLP then introduces transformer-based architectures through the Transformers library. It covers fine-tuning and inference for modern language tasks such as classification, summarization, and named entity recognition.
Who Should Attend
Data Scientists, Data Engineers, Machine Learning Engineers, MLOps professionals
What Attendees Will Learn
Upon completing AI for Natural Language Processing, participants will be able to:
- Build an in-depth understanding of how to build AI solutions using text data
- Learn the approaches to various natural language tasks, using both traditional machine learning and deep neural networks
- Gain hands-on experience building natural language models with powerful tools such as SpaCy, SentencePiece and Transformers
- Develop the skills and expertise to build machine learning solutions with natural language data
Prerequisites
Participants should have intermediate programming skills, preferably in Python, along with a strong understanding of mathematics and statistics. They should also be familiar with machine learning and neural network concepts and have a solid grasp of computer science fundamentals. Familiarity with common operating systems and basic command-line operations is also expected, along with a willingness to review pre-course materials.
These prerequisites help participants engage effectively with the course material and hands-on labs, making the learning experience more rewarding.