Applied Machine Learning

This three-day training introduces and helps you master key machine learning techniques applied to real-world use cases. You will learn to prepare your data, train and evaluate regression, classification, and clustering models, then optimize your workflows with scikit-learn.

Learning Objectives:

• Understand the ML project lifecycle: data collection, preparation, modeling, and evaluation.
• Implement supervised regression algorithms (linear regression, logistic regression).
• Apply classification methods (decision trees, RandomForest, SVM) and evaluate their performance.
• Explore clustering and dimensionality reduction (K-Means, PCA).
• Assemble scikit-learn pipelines to automate processing and cross-validation.

Target Audience:
Data analysts, beginner data scientists, or anyone seeking foundational machine learning training.

Duration:
3 days (9:00–12:30 & 13:30–17:00).

Number of Participants:
Maximum 12 participants.

Prerequisites:
Basic Python knowledge and experience with pandas data manipulation.

Program:

Day 1: Data Preparation & Regression

• Introduction to scikit-learn and the ML workflow.
• Data cleaning, transformation, and feature engineering.
• Implement linear and logistic regression.
• Workshop: evaluate and interpret regression results.

Day 2: Classification & Clustering

• Decision trees and RandomForest: principles and implementation.
• SVM and evaluation metrics.
• Clustering: K-Means algorithm and cluster analysis.
• Workshop: segment data and interpret clusters.

Day 3: Pipelines & Optimization

• Create scikit-learn pipelines and use ColumnTransformer.
• Cross-validation, GridSearchCV, and RandomizedSearchCV.
• Dimensionality reduction with PCA.
• Final workshop: build a complete workflow and tune hyperparameters.

Pricing:

• Open enrollment: €1,800 excl. VAT per participant
• In-house: on request, tailored to your needs

Methods Employed:

• Theoretical presentations and demonstrations
• Guided practical workshops
• Mini-application project

Evaluation:

• Positioning and validation quizzes
• Mini-project review and personalized feedback

Accessibility:
Our sessions are accessible to people with disabilities. Please contact accessibility@eurekia-learning.com to arrange accommodations.

Contacts:

• Quality referent: Jihane Khouzaimi (06 69 53 77 75 – contact@eurekia-learning.com)
• Pedagogical referent: Hatim Khouzaimi

Note:
This training will equip you to implement reliable and reproducible ML solutions in your business projects.