Machine Learning for Beginners - A Curriculum

The page presents a 12-week, 24-lesson curriculum on machine learning that focuses on classic machine learning with Scikit-learn and avoids deep learning. It is organized around projects, quizzes, assignments, and a world-cultures theme, with lessons that move from basics to reinforcement learning and a final section on real-world applications.

Reading notes

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• The curriculum covers classic machine learning and uses Scikit-learn as the primary library.
• It avoids deep learning, which is reserved for a forthcoming AI for Beginners curriculum.
• The lessons are designed to be paired with a forthcoming Data Science for Beginners curriculum.
• Each lesson includes pre- and post-lesson quizzes, written instructions, a solution, and an assignment.
• The pedagogy is project-based, with students learning by building.
• Students are asked to fork the repo, work through quizzes, lessons, challenges, and assignments, then use the discussion board and PAT rubric.
• Teachers are given suggestions for how to use the curriculum.
• The curriculum includes optional sketchnotes, optional videos, knowledge checks, supplemental reading, and quizzes.
• The lesson list moves through introduction, regression, web apps, classification, clustering, natural language processing, time series forecasting, and reinforcement learning.
• The postscript covers real-world ML scenarios and applications.
• The site can be used offline with Docsify, and a PDF version is available.