A set of resources I came across by chance: "Iris Book: From Addition, Subtraction, Multiplication, and Division to Machine Learning" . This set of books integrates "Calculus" , "Linear Algebra" , "Probability and Statistics" , "Optimization Problems" , "Geometry" , "Data Science" , "Machine Learning" and so on.
❝"Author's Preface:" The purpose of my creation of this "atlas" is very simple, to give everyone a new motivation to "learn and use mathematics" - data science and machine learning. Let everyone be interested in learning mathematics, understand it, think about it, be more confident, use it, and even feel the beauty of mathematics.
In order to let everyone learn mathematics, use mathematics, and even fall in love with mathematics, the author has taken great pains. When creating this set of books, the author tried his best to overcome the various drawbacks of traditional mathematics textbooks, so that everyone can be interested, understandable, thoughtful, more confident and useful when learning.
To this end, the series highlights the following characteristics in content creation:
"Mathematics + Art" - full-color illustrations, extreme visualization, let mathematical ideas come to life on paper, vivid and interesting, understand at a glance, and at the same time improve everyone's data thinking, geometric imagination, and artistic sense;
"Zero-based" - learn Python programming from scratch, from writing the first line of code to building data science and machine learning applications;
"Knowledge Network" - break the barriers between mathematics sections, let everyone see the connection between arithmetic, algebra, geometry, linear algebra, calculus, probability statistics and other sections, and weave a dense mathematical knowledge network;
"Hands-on programming" - teach people how to fish rather than fish, write codes with everyone, create mathematical animations and interactive apps with Streamlit;
"Learning ecology" - Construct an independent inquiry learning ecological environment "micro-class video + paper books + e-books + code files + visualization tools + mind maps" to provide various high-quality learning resources;
"Theory + Practice" - from addition, subtraction, multiplication, and division to machine learning, the content of the series is arranged from shallow to deep, spiraling up, taking into account both theory and practice; learn mathematics while learning mathematics, and solve practical problems while learning mathematics.
Although this book advertises "from addition, subtraction, multiplication and division to machine learning", it is recommended that readers and friends have at least high school mathematics knowledge. If the reader is studying or has studied college mathematics (calculus, linear algebra, probability and statistics), this set of books is easier to read.
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The core features of this set of resources: "full-color diagrams + Python programming + making App + micro-classes" .
The book is completely open source, and Pythonthe PDF download address of documents and manuscripts:
https://github.com/Visualize-ML/
There are 7 volumes in the whole series:
"Programming is not difficult" Book1_Python-For-Beginners
"Visible Beauty" Book2_Beauty-of-Data-Visualization
"Elements of Mathematics" Book3_Elements-of-Mathematics
"Matrix Power" Book4_Power-of-Matrix
"Statistics to Jane" Book5_Essentials-of-Probability-and-Statistics
"Data Youdao" Book6_First-Course-in-Data-Science
"Machine Learning" Book7_Visualizations-for-Machine-Learning
❝It's a pity that the author has not yet completed all of it, but I have read the algebra part, which is easy to understand and very suitable for beginners. Everyone read the updated ones, and you can remind them when the time comes (×).
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Here is a preview of some of the contents of the book
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