This course is a practical, project-based introduction to Machine Learning explained in Moroccan Darija.

You will learn how to build real Machine Learning models using Python, starting from data understanding all the way to model deployment.

Instead of focusing only on theory, this course is fully hands-on. You will work on real datasets and build complete projects in Regression, Classification, and Unsupervised Learning.

You will also learn how to:

• Clean and prepare real-world data

• Train and evaluate different Machine Learning models

• Improve model performance using proper techniques

• Compare multiple algorithms and select the best one

• Deploy your final model on a website

By the end of the course, you will understand the full Machine Learning pipeline and be able to build your own end-to-end projects.

What You’ll Learn

• Build Machine Learning models using Python and Scikit-Learn

• Work with real datasets using Pandas and NumPy

• Train Regression, Classification, and Clustering models

• Evaluate and compare multiple Machine Learning algorithms

• Apply data preprocessing techniques (encoding, scaling, cleaning)

• Deploy a Machine Learning model

• Understand the full end-to-end ML workflow

Who this course is for

• Beginners in Machine Learning

• Python users who want to apply ML in real projects

• Students learning Data Science

• Developers transitioning into Machine Learning

• Anyone who prefers practical learning over theory

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Requirements

• Basic knowledge of Python programming

• Basic understanding of Pandas

• A computer with Python installed (Anaconda or Jupyter Notebook)

• No prior Machine Learning experience required