Project Title

ML-For-Beginners — A comprehensive 12-week curriculum for beginners to learn classic Machine Learning

Overview

ML-For-Beginners is a beginner-friendly, 12-week curriculum designed to teach classic Machine Learning techniques using Scikit-learn. Developed by Microsoft Cloud Advocates, it offers 26 lessons, 52 quizzes, and project-based assignments to help learners grasp fundamental ML concepts. The curriculum focuses on a global perspective, applying ML techniques to data from various world cultures.

Key Features

12-week, 26-lesson curriculum covering classic Machine Learning
52 quizzes for pre- and post-lesson assessments
Project-based assignments to apply learned concepts
Global perspective with data from diverse cultures
Solution code provided in /solution folders for each project-oriented lesson

Use Cases

Use case 1: Beginners looking to learn Machine Learning fundamentals
Use case 2: Educators seeking a structured curriculum for teaching ML
Use case 3: Self-learners who prefer a project-based learning approach

Advantages

Advantage 1: Comprehensive curriculum covering a wide range of ML topics
Advantage 2: Project-based learning to reinforce understanding and retention
Advantage 3: Global perspective makes learning engaging and culturally diverse

Limitations / Considerations

Limitation 1: Focuses on classic Machine Learning, not deep learning
Limitation 2: Requires a basic understanding of Python and programming concepts

AI-For-Beginners: A related curriculum by Microsoft that covers deep learning and AI basics. It differs in its focus on AI and deep learning techniques.
Data Science for Beginners: Another curriculum by Microsoft that complements ML-For-Beginners by covering data science fundamentals. It provides a broader context for understanding data-driven techniques.