Master Python to analyze, model, and deploy AI solutions
This course covers the basics of the language, data manipulation, Machine Learning and Deep Learning algorithms, as well as natural language processing and computer vision techniques. Each module is accompanied by practical workshops for concrete implementation.
Is it for you ?
Developers, engineers, data scientists, anyone wishing to retrain in AI
Prerequisites
Basic programming knowledge (preferably Python). Notions of mathematics/statistics (linear algebra, probability).
What You'll Walk Away With
- ✓ Manipulate and transform data using NumPy and Pandas for reliable analysis
- ✓ Explore and visualize datasets to uncover actionable patterns and correlations
- ✓ Build supervised and unsupervised machine learning models for real-world use cases
- ✓ Develop deep learning models for computer vision and natural language processing
- ✓ Deploy AI models via Flask APIs and operationalize their usage
Training content
1 Syntax and basic structures
- Variables, types, loops, functions
- Lists, dictionaries, tuples
2 Object‑oriented programming
- Classes, inheritance, encapsulation
3 Work environments
- Jupyter Notebook, VS Code, Google Colab
Workshop 1: Creation of a mini object‑oriented Python project
4 NumPy and Pandas
- Multidimensional arrays
- Data cleaning and transformation
5 Visualization with Matplotlib and Seaborn
- Statistical charts
- Correlations and distributions
Workshop 2: Exploratory analysis of a dataset (Titanic, Iris, etc.)
6 Supervised learning
- Linear and logistic regression
- Decision trees, Random Forest
7 Unsupervised learning
- K‑means, PCA, hierarchical clustering
8 Model evaluation
- Accuracy, precision, recall, F1‑score
Workshop 3: Implementation of a complete classification model
9 Artificial neural networks
- Perceptron, MLP, activation functions
10 Training and validation
- Overfitting, early stopping, dropout
11 Image processing
- CNNs, image recognition
Workshop 4: Creation of an image recognition model (MNIST)
12 Natural language processing (NLP)
- Tokenization, TF‑IDF, Word Embeddings
- Text classification models
13 Deploying AI models
- Saving with Pickle/Joblib
- Flask API for inference
14 Final project
- Choice of a real‑world use case (NLP, vision, prediction)
- Presentation of results
Workshop 5: Deployment of an AI model via a Flask API
📌 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.