Pythonic AI

Mathematics For Machine Learning

The table of contents breaks down as follows:

Part I: Mathematical Foundations
- Introduction and Motivation
- Linear Algebra
- Analytic Geometry
- Matrix Decompositions
- Vector Calculus
- Probability and Distribution
- Continuous Optimization

Part II: 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

https://mml-book.github.io

#machinelearning #artificialintelligence #book

@pythonicAi

www.tgoop.com/pythonicAI/1010

341 viewsSoroush Hashemi Far, Apr 23, 2020 at 09:40

Mathematics For Machine Learning

The table of contents breaks down as follows:

Part I: Mathematical Foundations
- Introduction and Motivation
- Linear Algebra
- Analytic Geometry
- Matrix Decompositions
- Vector Calculus
- Probability and Distribution
- Continuous Optimization

Part II: 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

https://mml-book.github.io

#machinelearning #artificialintelligence #book

@pythonicAi

BY Pythonic AI