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Arik Chakma
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* refactor: clean to roadmap content * refactor: move shared helpers into scripts/lib
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Maths & Statistics
Mathematics and statistics provide the foundational principles for understanding and building machine learning models. These disciplines offer the tools to analyze data, quantify uncertainty, and optimize model performance. Key areas include linear algebra for data representation and manipulation, calculus for optimization algorithms, probability theory for handling uncertainty, and statistical inference for concluding data.
Visit the following resources to learn more:
- @book@Introductory Statistics
- @article@Computer Science 70, 001 - Spring 2015 - Discrete Mathematics and Probability Theory
- @article@Discrete Mathematics By IIT Ropar NPTEL
- @article@Introduction to Statistics
- @article@How to Learn the Math Needed for Data Science
- @video@Lec 1 | MIT 6.042J Mathematics for Computer Science, Fall 2010
- @video@Discrete Mathematics by Shai Simonson (19 videos)
- @video@tatistics - A Full University Course on Data Science Basics