Prepare data, train models, and interpret results effectively
This intensive training course explores the fundamentals of data mining and machine learning, focusing on techniques for extracting knowledge from data, predictive modeling, and performance evaluation. It combines theory and practical workshops to enable participants to build intelligent and interpretable models.
Is it for you ?
Data Analysts, Junior Data Scientists, Developers, Technical Project Managers
Prerequisites
Basic knowledge of statistics and programming (Python recommended)
What You'll Walk Away With
- ✓ Prepare and transform data (cleaning, normalization, feature engineering) for reliable models
- ✓ Implement supervised and unsupervised algorithms using Scikit-learn
- ✓ Evaluate model performance with appropriate metrics (accuracy, F1-score, ROC)
- ✓ Interpret model outputs using tools such as SHAP and LIME
- ✓ Identify bias and apply ethical practices in Machine Learning projects
Training content
1 Fundamentals
- Definition and challenges
- Life cycle of a data mining project
- Cleaning, transformation, normalization
- Feature engineering
- Workshop 1: Preparing a real dataset
2 Machine learning algorithms
- Linear regression, logistic regression
- Decision trees, Random Forest
- K-means, PCA, hierarchical clustering
- Workshop 2: Classifying a dataset with Scikit-learn
3 Evaluation and interpretation
- Accuracy, precision, recall, F1-score
- ROC curves, confusion matrix
- SHAP, LIME
- Ethics and algorithmic bias
- Workshop 3: Optimizing and interpreting a predictive model
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📌 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.
