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What is this?
This is the companion website for the book 'Mathematics For Machine Learning', providing exercises and Jupyter notebooks that go along with the book's chapters.
This is the companion website for the book 'Mathematics For Machine Learning', providing exercises and Jupyter notebooks that go along with the book's chapters.Is this the full book or just supplementary material?It's supplementary material, not the book text itself — the full book is published separately by Cambridge University Press, and this repo hosts the accompanying exercises and notebooks.
It's supplementary material, not the book text itself — the full book is published separately by Cambridge University Press, and this repo hosts the accompanying exercises and notebooks.How do I run the notebooks?You can run them live via the hosted link in the README, or open them directly in Google Colab — no local setup is required to try them.
You can run them live via the hosted link in the README, or open them directly in Google Colab — no local setup is required to try them.Do I need to install Python or Jupyter myself?No, since the notebooks run live on Binder or Google Colab you can use them straight from your browser without installing anything locally.
No, since the notebooks run live on Binder or Google Colab you can use them straight from your browser without installing anything locally.Does this book cover advanced machine learning techniques?No, it deliberately skips advanced ML methods since many other books already cover those; it focuses only on the math skills needed to read them.
No, it deliberately skips advanced ML methods since many other books already cover those; it focuses only on the math skills needed to read them.How is the book structured?It's split into two parts: Part 1 covers mathematical foundations with exercises, and Part 2 covers example machine learning algorithms built on those foundations, with accompanying Jupyter notebooks.
It's split into two parts: Part 1 covers mathematical foundations with exercises, and Part 2 covers example machine learning algorithms built on those foundations, with accompanying Jupyter notebooks.Which ML algorithms are covered in the notebooks?The notebooks currently cover Linear Regression, Principal Component Analysis (PCA), and Gaussian Mixture Models (GMM), each with a tutorial notebook and a solution notebook.
The notebooks currently cover Linear Regression, Principal Component Analysis (PCA), and Gaussian Mixture Models (GMM), each with a tutorial notebook and a solution notebook.Who is this book really for?It's aimed at people who want to build the math skills needed to read other, more advanced machine learning books, rather than people already comfortable with that math.
It's aimed at people who want to build the math skills needed to read other, more advanced machine learning books, rather than people already comfortable with that math.What are similar projects?Book4_Power-of-Matrix, Book-Mathematical-Foundation-of-Reinforcement-Learning, The-Art-of-Linear-Algebra, awesome-math, Probabilistic-Programming-and-Bayesian-Methods-for-Hackers, awesome-datascience, the-craft-of-selfteaching, MathModel
Book4_Power-of-Matrix, Book-Mathematical-Foundation-of-Reinforcement-Learning, The-Art-of-Linear-Algebra, awesome-math, Probabilistic-Programming-and-Bayesian-Methods-for-Hackers, awesome-datascience, the-craft-of-selfteaching, MathModelWhich topics is it in?linear-algebra, textbook, science, students, mathematics, statistics, academic, jupyter notebook, data-science, book, beginner-friendly, guide
linear-algebra, textbook, science, students, mathematics, statistics, academic, jupyter notebook, data-science, book, beginner-friendly, guide