:og:description: Dive deep into the world of mathematics for machine learning and data science with comprehensive notes from DeepLearning.ai's course, covering linear algebra, calculus, PCA, SVM, and more.

Mathematics for Machine Learning and Data Science 
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Notes from DeepLearning.ai's course `Mathematics for Machine Learning and Data Science <https://www.coursera.org/specializations/mathematics-for-machine-learning-and-data-science>`_ taught by Luis Serrano from Coursera.

⚠️ These are not the official notebooks from the course. 

Although I was largely inspired by the course, I've also used other sources like Khan Academy, Wikipedia to name a few.

I've also included some extra content like PCA and SVM from scratch, the actual implementation of back-propagation (credit: `Deep Learning Specialization <https://www.coursera.org/specializations/deep-learning>`_), the relationship between Newton's method and Taylor series, the Lagrange multiplier method, etc.

I did not include notes on statistics because it's a topic I didn't need to refresh as much as linear algebra and calculus.

.. toctree::
   :hidden:
   :caption: Topics

   Linear Algebra <linear_algebra/index>
   Calculus <calculus/index>

.. meta::
   :description: Dive deep into the world of mathematics for machine learning and data science with comprehensive notes from DeepLearning.ai's course, covering linear algebra, calculus, PCA, SVM, and more.