• Master fundamental concepts of data preparation for applying techniques
• Understand the fundamentals of training and evaluation
• Master core concepts and apply supervised (parametric & non-parametric) and unsupervised learning techniques
• Learn how to build, train, and evaluate models

• Data Preparation and Feature Engineering : analyzing data properties, applying feature engineering and data preprocessing for algorithm training
• Parametric and non-parametric techniques ; supervised and unsupervised learning : exploring key differences and applications
• Supervised Learning (parametric): understanding parametric models (regressions, neural networks/deep learning)
• Supervised Learning (non parametric): techniques like decision trees, random forest, xboost and KNN and performance evaluation

This training is designed to provide a deep understanding of the theoretical foundations of advanced machine learning, focusing on the principles that drive model performance and real-world applications. Rather than a coding-intensive course, this program prioritizes conceptual mastery, ensuring participants can interpret, evaluate, and apply machine learning models effectively. Pre-written code is provided to illustrate key algorithms, allowing participants to focus on understanding how and why models work rather than spending time on implementation details. By the end of the course, attendees will be equipped to leverage existing models, make informed decisions on their use, and integrate machine learning solutions into their business context without requiring extensive programming

Our instructors are highly experienced in machine learning and possess strong expertise in Python development. With a focus on practical, hands-on applications, they guide participants through the complexities of building and fine-tuning machine learning models. All participants will have full access to course materials, including code examples, datasets, and step-by-step tutorials, ensuring a comprehensive learning experience.