48 minutes agoDevelopmentBuild real-world Machine Learning models using Python, from regression to deployment, explained in Moroccan Darija
Course Description
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
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
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