Natural Languange Processing with Python

Course description:

This Natural Language Processing (NLP) course provides a comprehensive introduction to key NLP techniques and concepts, helping participants develop essential NLP skills. 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 hands-on exercises and work on projects to reinforce their understanding and gain practical experience in NLP.

Prerequisites:

Basic programming skills (familiarity with Python)
Fundamental understanding of machine learning techniques
Experience with ML libraries in Python
Basic knowledge of Deep Learning

Who is it for:

Students and researchers interested in artificial intelligence, machine learning, and natural language processing.
Software developers and engineers who want to expand their NLP skills and build applications based on text understanding and generation.
Data analytics specialists and data scientists who want to integrate text analysis into their analytical designs and models.
Artificial intelligence and machine learning professionals who want to specialize in NLP to stay competitive in their careers.
Entrepreneurs and product managers who want to explore NLP-related business opportunities and innovations in different industries.

What will you learn:

The history, applications, and importance of natural language processing (NLP).
Text processing techniques such as tokenization rooting, lematization and how they are used to prepare data for analysis.
The different methods of word representation in the context of NLP, including one-hot encoding, Bag-of-Words (BoW), TF-IDF and Word Embeddings.
How to apply text processing in practice using Python and NLP libraries.
Syntax and grammatical analysis, including constitutional/dependent parsing and part-of-speech tagging (POS) and entity name recognition (NER).
Techniques for analyzing feelings and classifying texts, along with strategies for selecting relevant characteristics.
How to perform sentiment analysis using machine learning algorithms and deep learning.
Seq2Seq models and associated key concepts such as Encoder-Decoder and attention mechanism, as well as relevant application areas.
Pre-trained models such as BERT and the GPT family (including 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.

Course module structure

• Introduction to NLP: History, Applications and Meaning.
• Text processing: tokenization, root, lematization, etc.
• Word Representation: One-hot, BoW, TF-IDF, Word Embeddings.
• Practical application: text processing using Python and NLP libraries.
• Syntax and grammatical analysis: Constitutional/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 (including ChatGPT).
• Evaluation metrics, challenges and future directions in NLP.

Certification:

Certificate of completion.

€ 840

DURATION: 2 days
SKILL LEVEL: Associate
LECTURES: 10 lessons

Course description:

Prerequisites:

Basic programming skills (familiarity with Python)
Fundamental understanding of machine learning techniques
Experience with ML libraries in Python
Basic knowledge of Deep Learning

Who is it for:

Students and researchers interested in artificial intelligence, machine learning, and natural language processing.
Software developers and engineers who want to expand their NLP skills and build applications based on text understanding and generation.
Data analytics specialists and data scientists who want to integrate text analysis into their analytical designs and models.
Artificial intelligence and machine learning professionals who want to specialize in NLP to stay competitive in their careers.
Entrepreneurs and product managers who want to explore NLP-related business opportunities and innovations in different industries.

What will you learn:

The history, applications, and importance of natural language processing (NLP).
Text processing techniques such as tokenization rooting, lematization and how they are used to prepare data for analysis.
The different methods of word representation in the context of NLP, including one-hot encoding, Bag-of-Words (BoW), TF-IDF and Word Embeddings.
How to apply text processing in practice using Python and NLP libraries.
Syntax and grammatical analysis, including constitutional/dependent parsing and part-of-speech tagging (POS) and entity name recognition (NER).
Techniques for analyzing feelings and classifying texts, along with strategies for selecting relevant characteristics.
How to perform sentiment analysis using machine learning algorithms and deep learning.
Seq2Seq models and associated key concepts such as Encoder-Decoder and attention mechanism, as well as relevant application areas.
Pre-trained models such as BERT and the GPT family (including 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.