Analyze, model, and leverage textual data with AI
This training course offers an immersion in natural language processing (NLP) techniques used to analyze, understand, and generate text in human language.
Is it for you ?
• AI developers and engineers
• Data scientists
• IT project managers
Prerequisites
• Basic knowledge of Python.
• Understanding of machine learning or data science.
• Comfortable with digital tools and development environments.
What You'll Walk Away With
- ✓ Prepare and clean textual data (tokenization, lemmatization, normalization)
- ✓ Represent text using appropriate methods (TF-IDF, embeddings)
- ✓ Build and evaluate models for text classification and sentiment analysis
- ✓ Extract key information such as named entities, relationships, and keywords
- ✓ Develop and deploy NLP applications, including chatbot solutions with Transformers
Training content
1 Fundamentals of NLP
Chapter 1: Introduction to NLP
- Definition and history
- Areas of application: healthcare, HR, legal, marketing, etc.
Chapter 2: Text Preprocessing
- Tokenization, lemmatization, stemming
- Text data cleaning
- Stop words and normalization
Chapter 3: Text Representation
- Bag of Words, TF-IDF
- Word embeddings: Word2Vec, GloVe
Workshop 1: Cleaning and vectorizing a text corpus
- Manipulating a dataset (e.g., customer reviews or resumes) with NLTK and scikit-learn
2 Modeling and information extraction
Chapter 4: Text classification models
- Naive Bayes, SVM, neural networks
- Performance evaluation
Chapter 5: Information extraction
- Named entity recognition (NER)
- Extraction of relationships and keywords
Chapter 6: Sentiment analysis
- Supervised methods and lexicons
- Use cases: customer feedback, social networks
Workshop 2: Creating a sentiment classification model
- Training a model on product reviews or user comments
3 Advanced NLP and integration
Chapter 7: NLP with Transformer models
- Introduction to BERT, GPT, RoBERTa
- Fine-tuning and transfer learning
Chapter 8: Text generation and chatbots
- Generative models
- Designing a simple conversational assistant
Chapter 9: Deployment and integration
- NLP APIs (Hugging Face, spaCy)
- Integration into a web or mobile application
Workshop 3: Creating a mini chatbot with Hugging Face Transformers
- Developing a conversational assistant to answer HR or medical questions
📌 Practical information
Our training sessions are offered in Montreal or Quebec City, in person or in a virtual classroom. Dates and locations are specified when you select your session below. If you have any questions, check out our FAQ.
