Stage 2: Classic Machine Learning
A classic 12-week, 26-lesson machine learning curriculum for beginners developed by Microsoft, learning machine learning through practical application with cultural data from around the world.
ML-For-Beginners Project Detailed Introduction
Project Overview
ML-For-Beginners is a comprehensive machine learning tutorial project for beginners developed by the Microsoft Cloud Advocate team. It is a 12-week, 26-lesson, 52-quiz classic machine learning curriculum focused on exploring machine learning using cultural data from around the world.
Core Features
- 🌍 Global Perspective: Learn machine learning through cultural data from around the world.
- 📚 Systematic Curriculum: A complete 12-week curriculum with 26 detailed lessons.
- 🧪 Practice-Oriented: Project-based teaching method, learning by doing.
- 🔍 Assessment System: Includes 52 quizzes, with pre- and post-lesson assessments.
- 🎯 Classic Machine Learning: Primarily uses the Scikit-learn library, avoiding deep learning content.
Teaching Philosophy
Two Core Teaching Principles
- Hands-on Practice: Ensure all content is based on project-based practical learning.
- Frequent Assessment: Ensure learning effectiveness through pre- and post-lesson quizzes.
Course Structure
Each lesson includes the following components:
- Optional sketchnote
- Optional supplementary video
- Video demonstration (some lessons)
- Pre-lesson warm-up quiz
- Written lesson content
- Project-based step-by-step guide
- Knowledge checkpoints
- Challenge exercises
- Supplementary reading materials
- Assignment
- Post-lesson quiz
Complete Course Outline
Lessons 1-4: Introduction to Machine Learning Fundamentals
- Lesson 1: Introduction to Machine Learning
- Lesson 2: History of Machine Learning
- Lesson 3: Fairness in Machine Learning
- Lesson 4: Machine Learning Techniques
Lessons 5-8: Regression Analysis
- Lesson 5: Introduction to Linear Regression
- Lesson 6: Predicting North American Pumpkin Prices (Linear Regression)
- Lesson 7: Predicting Pumpkin Prices (Polynomial Regression)
- Lesson 8: Introduction to Logistic Regression
Lesson 9: Web Application Development
- Lesson 9: Building Web Applications
Lessons 10-13: Classification Algorithms
- Lesson 10: Introduction to Classification Algorithms
- Lesson 11: Classifying Asian and Indian Cuisine
- Lesson 12: More Classification Algorithms
- Lesson 13: Recommendation Systems
Lessons 14-15: Clustering Algorithms
- Lesson 14: Introduction to Clustering Algorithms
- Lesson 15: Exploring Nigerian Music Taste
Lessons 16-20: Natural Language Processing
- Lesson 16: Introduction to Natural Language Processing
- Lesson 17: Common NLP Tasks
- Lesson 18: Translation and Sentiment Analysis
- Lesson 19: Analyzing Romantic Hotel Reviews (1)
- Lesson 20: Analyzing Romantic Hotel Reviews (2)
Lessons 21-23: Time Series Analysis
- Lesson 21: Introduction to Time Series Forecasting
- Lesson 22: ARIMA Time Series Forecasting
- Lesson 23: Support Vector Regression Time Series Forecasting
Lessons 24-25: Reinforcement Learning
- Lesson 24: Reinforcement Learning and Q-Learning
- Lesson 25: Reinforcement Learning with Gym
Lesson 26: Real-World Applications
- Lesson 26: Machine Learning Applications in the Real World
Tech Stack and Tools
Main Programming Languages
- Python: Primarily using Python for teaching
- R: Some lessons provide R language versions
Core Libraries and Frameworks
- Scikit-learn: Main machine learning library
- Python Data Science Ecosystem: pandas, numpy, matplotlib, etc.
Supporting Tools
- Jupyter Notebook: Interactive programming environment
- R Markdown: Used for R language courses
- Quiz App: Dedicated quiz application
Learning Resources
Multimedia Support
- Sketchnotes to help understand concepts
- Video tutorials for supplementary explanations
- Demonstrations of practical projects
Assessment System
- 52 quizzes (each containing 3 questions)
- Pre-lesson warm-up quizzes
- Post-lesson consolidation quizzes
- Project practice assessments
Community Support
- GitHub Discussions
- Learning Progress Tracker (PAT)
- Peer learning and feedback
Learning Recommendations
Target Audience
- Machine learning beginners
- Learners with basic programming experience
- Individuals who want to systematically learn classic machine learning
Learning Methods
- Fork the project to your personal GitHub account
- Complete each lesson in order
- Complete all practical projects
- Participate in community discussions and peer learning
Companion Courses
- AI for Beginners: Deep learning content
- Data Science for Beginners: Data science fundamentals
Project Team
Main Authors
- Jen Looper, Stephen Howell, Francesca Lazzeri
- Tomomi Imura, Cassie Breviu, Dmitry Soshnikov
- Chris Noring, Anirban Mukherjee, Ornella Altunyan
- Ruth Yakubu, Amy Boyd
Illustration Design
- Tomomi Imura, Dasani Madipalli, Jen Looper
Student Ambassador Contributors
- Rishit Dagli, Muhammad Sakib Khan Inan, Rohan Raj
- Alexandru Petrescu, Abhishek Jaiswal, Nawrin Tabassum
- Ioan Samuila, Snigdha Agarwal
Usage
Get the Project
git clone https://github.com/microsoft/ML-For-Beginners.git
Run Documentation Locally
npm i docsify-cli -g
docsify serve
Access Online
- Directly access the GitHub repository
- View the Microsoft Learn collection
- Watch YouTube video tutorials
License and Contribution
The project follows an open-source license and welcomes community contributions. The project provides detailed contribution guidelines and code of conduct, supporting multilingual translation and content improvement.