IA113
Artificial Intelligence

IA: Data Mining & Machine Learning

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.

Objectives

The objective of this training is to gain an understanding of the principles of data mining and machine learning, to know how to apply and evaluate different analysis algorithms, and to put this knowledge into practice using tools such as Scikit-learn.

Is it for you ?

Data Analysts, Junior Data Scientists, Developers, Technical Project Managers

Prerequisite

Basic knowledge of statistics and programming (Python recommended)

Your benefits

  • 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
  • Content

    Introduction to Data Mining

    Chapter 1: Fundamentals

    • Definition and challenges
    • Life cycle of a data mining project

    Chapter 2: Data preparation

    • Cleaning, transformation, normalization
    • Feature engineering

    Workshop 1: Preparing a real dataset

    See more + / -

    Machine learning algorithms

    Chapter 3: Supervised Learning

    • Linear regression, logistic regression
    • Decision trees, Random Forest

    Chapter 4: Unsupervised Learning

    • K-means, PCA, hierarchical clustering

    Workshop 2: Classifying a dataset with Scikit-learn

    Evaluation and interpretation

    Chapter 5: Model evaluation

    • Accuracy, precision, recall, F1-score
    • ROC curves, confusion matrix

    Chapter 6: Interpretability and bias

    • SHAP, LIME
    • Ethics and algorithmic bias

    Workshop 3: Optimizing and interpreting a predictive model

    💡 Useful information

    Our training sessions are offered in Montreal or Quebec City, in person or in virtual format. Dates and locations are provided when you select your session below. If you have any questions regarding registration, schedules, the language of instruction, or cancellation policies, please consult our FAQ .

    Duration
    3 days
    Schedule
    9h to 16h
    Regular fee
    $1,485
    Preferential fee A preferential rate is offered to public institutions, to members of certain professional organizations as well as to companies that do a certain amount of business with Technologia. To know more, please read the "Registration and rates" section on our FAQ page. Please note that preferential rates are not available for online training courses. Discounts cannot be combined with other offers.
    $1,335
    Private or personalized training

    Do you have several employees interested in the same training course? Whether in person at your offices or remotely in virtual mode, we offer private training courses tailored to your team's needs. Group rates are available. Contact us for more details or request a quote online.

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    Request in-company training

    Do you have several employees interested in the same training course? Whether in person at your offices or remotely in virtual mode, we offer private training courses tailored to your team's needs. Group rates are available. Contact us for more details or request a quote online.

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