Ultimate Math Guide for Machine Learning Enthusiasts
Exciting news for everyone diving into the world of Machine Learning! I stumbled upon a fantastic resource that consolidates all the essential math needed for ML, and guess what?
It’s free (download here). Tailored for those keen on grasping the math behind ML algorithms.
Minimum prerequisite: High school-level math knowledge. Not there yet? No worries! There are countless free resources and YouTube tutorials to catch up.
The book is written to motivate people to learn mathematical concepts essential for machine learning. It doesn’t aim to cover advanced machine learning techniques but provides the necessary mathematical skills to read other books that do. The book was published by Cambridge University Press in April 2020
The book is divided into two parts:
- Mathematical Foundations:
- Introduction and Motivation
- Linear Algebra
- Analytic Geometry
- Matrix Decompositions
- Vector Calculus
- Probability and Distribution
- Continuous Optimization
- Central Machine Learning Problems:
- When Models Meet Data
- Linear Regression
- Dimensionality Reduction with Principal Component Analysis
- Density Estimation with Gaussian Mixture Models
- Classification with Support Vector Machines
Don’t miss out on the plethora of learning materials on their website – they are truly a treasure trove.
Machine learning has become really popular lately, with many great uses. This book explains the main math ideas behind it, like algebra, calculus, and probability. It’s good for both beginners and experts in machine learning.