ML-For-Beginners
by microsoft
Comprehensive Machine Learning Curriculum for Developers
A structured 12-week learning path with hands-on lessons, quizzes, and real-world ML projects using Python and scikit-learn.
- 83,080+ GitHub stars
- Built with Jupyter Notebook
- 26 comprehensive lessons organized into a 12-week progressive curriculum
- MIT License license
About This Project
This extensive machine learning curriculum offers developers a systematic approach to mastering classical ML algorithms through 26 carefully crafted lessons spanning three months. Each lesson combines theoretical foundations with practical implementations, reinforced by dual quizzes that test comprehension and application skills.
Built for hands-on learners, the course leverages Jupyter Notebooks to provide interactive coding experiences with popular libraries like scikit-learn. Students work through real datasets and build actual machine learning models, from regression and classification to clustering and natural language processing, ensuring they gain practical skills employers value.
The curriculum stands out by offering multiple programming language options, supporting both Python and R implementations. This flexibility allows developers to learn ML concepts in their preferred language while understanding fundamental algorithms that transcend specific tools. Each module includes pre-lesson and post-lesson assessments to track progress effectively.
With nearly 83,000 stars and contributions from Microsoft's education team, this resource has become a trusted foundation for developers transitioning into data science roles or engineers seeking to integrate ML capabilities into their applications.
Key Features
- 26 comprehensive lessons organized into a 12-week progressive curriculum
- 52 interactive quizzes (pre and post-lesson) for knowledge validation
- Jupyter Notebook-based exercises with real datasets and practical examples
- Dual language support with implementations in both Python and R
- Coverage of core ML algorithms including regression, classification, clustering, and NLP
How You Can Use It
Software engineers transitioning to machine learning or data science roles
Computer science students building foundational ML knowledge for academic projects
Self-taught developers creating a structured learning roadmap for ML algorithms
Technical teams upskilling on classical ML before advancing to deep learning
Who Is This For?
Developers and programmers with basic Python or R knowledge who want to learn machine learning fundamentals through structured, hands-on practice