LogoAIAny
Icon for item

ML for Beginners

ML for Beginners is a free, open-source curriculum developed by Microsoft, spanning 12 weeks with 26 lessons and 52 quizzes, focusing on classic machine learning using primarily Scikit-learn, avoiding deep learning. It uses a project-based approach with global cultural themes to explore topics like regression, classification, clustering, NLP, and time series forecasting.

Visit Website
Visit Website

Introduction

ML for Beginners: A Comprehensive Machine Learning Curriculum

Overview

ML for Beginners is an engaging, 12-week curriculum created by Microsoft Cloud Advocates, designed to introduce beginners to the fundamentals of classic machine learning (ML). Unlike deep learning-focused courses, this program emphasizes traditional ML techniques using the Scikit-learn library in Python (with optional R support). It consists of 26 lessons, each accompanied by pre- and post-lecture quizzes, hands-on projects, knowledge checks, challenges, assignments, and supplemental resources. The curriculum adopts a project-based pedagogy, where learners build real-world applications while traveling through diverse global cultures—such as analyzing North American pumpkin prices, Asian cuisines, Nigerian music, European hotels, and world power usage—to make learning culturally immersive and relatable.

Key Features and Structure
  • Duration and Content: Spanning 12 weeks, the course is divided into thematic groupings: Introduction to ML, Regression, Web Apps, Classification, Clustering, Natural Language Processing (NLP), Time Series, Reinforcement Learning, and a postscript on real-world ML applications. Each lesson includes optional sketchnotes, videos, and step-by-step guides.
  • Hands-On Learning: Learners fork the GitHub repository, complete quizzes via a dedicated app (52 quizzes total, 3 questions each), and build projects like regression models for pumpkin prices, classifiers for cuisine recommendations, clustering for music tastes, sentiment analysis on hotel reviews, ARIMA/SVR forecasting for power usage, and Q-Learning for reinforcement scenarios.
  • Multi-Language Support: Automated translations into over 40 languages (e.g., Arabic, Chinese, French, Hindi, Spanish) via GitHub Actions, ensuring global accessibility.
  • Community and Resources: Join the Microsoft Foundry Discord for discussions, or use the Discussion Board for Progress Assessment Tools (PATs). Video walkthroughs are available on the Microsoft Developer YouTube channel. Additional resources link to Microsoft Learn modules, troubleshooting guides, and PDFs for offline access.
  • Pedagogy: Rooted in project-based learning and frequent low-stakes quizzes to enhance retention. It addresses fairness in ML, historical context, and techniques early on. R versions of lessons use R Markdown for data science workflows.
Target Audience and Usage

Ideal for students, self-learners, or educators new to ML. Students can complete independently or in groups; teachers have suggestions for classroom integration. No prior coding experience is assumed beyond basic Python setup, with tools like Jupyter Notebooks used throughout.

Authors and Contributors

Led by Microsoft Cloud Advocates, with key authors including Jen Looper, Stephen Howell, Francesca Lazzeri, Tomomi Imura, Cassie Breviu, Dmitry Soshnikov, Chris Noring, Anirban Mukherjee, Ornella Altunyan, Ruth Yakubu, and Amy Boyd. Illustrations by Tomomi Imura, Dasani Madipalli, and Jen Looper. Special contributions from Microsoft Student Ambassadors like Rishit Dagli and Eric Wanjau (for R lessons).

Getting Started
  1. Fork the repository on GitHub.
  2. Clone it locally: git clone https://github.com/microsoft/ML-For-Beginners.git.
  3. Follow lesson instructions, starting with quizzes and progressing to projects.
  4. For offline docs, use Docsify to serve the site locally.

This curriculum fosters practical skills in ML model building, deployment (e.g., web apps), and ethical considerations, preparing learners for further AI studies like the companion 'AI for Beginners' or 'Data Science for Beginners' courses.

Impact and Extensions

With over 80,000 GitHub stars, it's a popular resource for ML education. It includes badges for licenses, contributors, issues, and welcomes PRs. Related Microsoft courses cover generative AI, cybersecurity, web dev, and more, all under open-source initiatives.

Back

Information

  • Websitegithub.com
  • AuthorsMicrosoft
  • Published date2020/06/01

Categories

Tags

More Items

Icon for item

Generative AI for Beginners

2023
Microsoft Cloud Advocates

A free 21-lesson course by Microsoft Cloud Advocates teaching beginners the fundamentals of building Generative AI applications. It covers concepts like Generative AI and LLMs, prompt engineering, chat apps, image generation, security, and more, with Python and TypeScript code samples supporting Azure OpenAI, OpenAI API, and open-source models. Features multi-language support, video intros, extra resources, and a Discord community.

tutorialcoursemicrosoftLLMopenai+3

CS231n: Deep Learning for Computer Vision

2015
Fei-Fei Li

Stanford’s 10-week CS231n dives from first principles to state-of-the-art vision research, starting with image-classification basics, loss functions and optimization, then building from fully-connected nets to modern CNNs, residual and vision-transformer architectures. Lectures span training tricks, regularization, visualization, transfer learning, detection, segmentation, video, 3-D and generative models. Three hands-on PyTorch assignments guide students from k-NN/SVM through deep CNNs and network visualization, and a capstone project lets teams train large-scale models on a vision task of their choice, graduating with the skills to design, debug and deploy real-world deep-learning pipelines.

foundationvision30u30coursetutorial

The First Law of Complexodynamics

2011
Scott Aaronson

This post explores why physical systems’ “complexity” rises, peaks, then falls over time, unlike entropy, which always increases. Using Kolmogorov complexity and the notion of “sophistication,” the author proposes a formal way to capture this pattern, introducing the idea of “complextropy” — a complexity measure that’s low in both highly ordered and fully random states but peaks during intermediate, evolving phases. He suggests using computational resource bounds to make the measure meaningful and proposes both theoretical and empirical (e.g., using file compression) approaches to test this idea, acknowledging it as an open problem.

foundationblog30u30tutorial