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Master Machine Learning in Darija: Hands-On Python Projects
Rating: 4.9 out of 5(14 ratings)
299 students

Master Machine Learning in Darija: Hands-On Python Projects

Build real-world Machine Learning models using Python, from regression to deployment, explained in Moroccan Darija
Last updated 5/2026
Arabic

What you'll learn

  • Understand the fundamentals of machine learning through real projects
  • Build your first regression, classification, and clustering models
  • Work with real datasets using Python libraries (Pandas, NumPy, Scikit-Learn)
  • Train and evaluate machine learning models step by step
  • Understand the full machine learning workflow from data to deployment-ready models

Course content

3 sections39 lectures1h 53m total length
  • 1- Welcome & Course Roadmap0:30
  • 2- What is Machine Learning?1:26
  • 3- Machine Learning Example: Spam Detection (f(x) = y)1:09
  • 4- Traditional Programming vs Machine Learning2:46
  • 5- How Machine Learning Learns2:51
  • 6- Types of Machine Learning1:33
  • 7- Supervised vs Unsupervised Learning1:27
  • 8- Introduction to Supervised Learning2:42
  • 9- Supervised Learning Problems: Classification vs Regression2:25

Requirements

  • Basic Python knowledge
  • Familiarity with Pandas for data manipulation
  • A computer with Python installed (Anaconda or any Python environment)
  • No prior experience in Machine Learning is required

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

Who this course is for:

  • Beginners in data science and Python
  • Students with basic Python knowledge looking to apply it to real-world projects
  • Anyone who prefers learning complex concepts explained simply in Moroccan Darija