Applied Machine Learning Foundations for Emerging Practitioners Using Python

Applied Machine Learning Foundations for Emerging Practitioners Using Python is designed as a project-driven bridge programme aimed at transitioning learners from foundational readiness to the ability to independently design, build and deploy machine learning projects.

This is a practical, skill developing course designed to progress efficiently from core concepts into practical machine learning workflows and modern AI tools. Given the rapid evolution of Artificial Intelligence and Machine Learning, learners entering this field require early exposure to applied, hands-on development rather than prolonged theoretical study.

The emphasis throughout is on learning by building. All laboratories are fully guided and delivered using industry-relevant platforms such as Google Colab, Kaggle and Visual Studio, enabling students to develop real, demonstrable projects.

On completion of this course, learners will be able to:

• Understand the fundamental concepts and terminology used in Machine Learning and Artificial Intelligence.
• Apply Python programming techniques for data analysis, manipulation, and visualization.
• Interpret basic statistical and linear algebra concepts relevant to machine learning models.
• Prepare and preprocess datasets for machine learning applications.
• Build, train, and evaluate supervised learning models for regression and classification problems.
• Apply unsupervised learning techniques to identify patterns and structure within unlabeled data.
• Develop end-to-end machine learning projects using industry-standard platforms such as Google Colab, Kaggle, and Visual Studio.
• Implement feature engineering techniques to improve model performance.
• Use Git and GitHub for version control and collaborative project management.
• Develop introductory AI applications using no-code and low-code tools.
• Implement Retrieval-Augmented Generation (RAG) workflows using Python.
• Demonstrate foundational understanding of agentic AI systems and modern AI development practices.
• Apply basic CI/CD concepts to support reproducible and scalable machine learning workflows.
• Design and present practical machine learning solutions addressing real-world problems.
• Compile a professional project portfolio suitable for further study, internships, or entry-level AI and ML roles.

Course Outline

Please click here to view the full course outline.

SCHEDULE & FEES

• Payment Plans are available.

Champs Fleurs

Please note: All information provided is subject to change without prior notice.