This Natural Language Processing (NLP) course provides a comprehensive introduction to key NLP techniques and concepts, helping participants develop essential skills in the field. The course explores methods of text processing, word representation, grammar analysis and the application of machine learning and deep learning algorithms to various NLP tasks such as sentiment analysis, text classification and machine translation. Participants will also learn about pre-trained models, such as BERT and the GPT family, and how to use them to build advanced NLP applications.
The course combines theory with practical application, using the Python programming language and popular NLP and machine learning libraries such as NLTK, spaCy, TensorFlow and PyTorch. Throughout the course, students will participate in practical exercises and work on projects to strengthen their understanding and gain practical experience in NLP.
• History, applications and importance of natural language processing (NLP).
• Text processing techniques such as tokenization, rooting, and lemmatization, and how they are used to prepare data for analysis.
• The different methods of representing words in the context of NLP, including one-hot encoding, Bag-of-Words (BoW), TF-IDF and Word Embeddings.
• How to put text processing into practice using Python and NLP libraries.
• Syntax and grammar analysis, including constitutional/dependent parsing and parts-of-speech (POS) tagging and entity name recognition (NER).
• Sentiment analysis and text classification techniques, along with strategies for selecting relevant features.
• How to perform sentiment analysis using machine learning and deep learning algorithms.
• Seq2Seq models and associated key concepts, such as Encoder-Decoder and the attention mechanism, as well as relevant application areas.
• Pre-trained models such as BERT and the GPT family (incl ChatGPT), and how they can be used in NLP problems.
• Methods for evaluating the performance of NLP models, challenges in the field and future directions in NLP development.
- Basic programming skills (familiarity with Python), fundamental understanding of machine learning techniques, experience with ML libraries in Python, basic knowledge of Deep Learning.
• Introduction to NLP: History, Applications and Meaning.
• Text processing: Tokenization, root, lemmatization, etc.
• Word representation: One-hot, BoW, TF-IDF, Word Embeddings.
• Practical application: Text processing using Python and NLP libraries.
• Syntax and Grammar Analysis: Constitutive/Dependent Parsing, POS Tagging, NER.
• Sentiment Analysis/Text Classification: Techniques and Feature Selection.
• Practical application: Sentiment analysis with ML algorithms and deep learning.
• Seq2Seq models: Encoder-Decoder, attention mechanism, applications.
• Pre-trained models: BERT, GPT family (incl ChatGPT).
• Evaluation metrics, challenges and future directions in NLP.
There are no recommendations at this time.
After this course, you will receive a certificate of completion.
