Math
behind Machine Learning
* If one wants to implement existing machine learning algorithms
from scratch or if someone wants to devise newer machine learning algorithms ,
then one would require a profound knowledge of probability , linear algebra ,
linear programming and multivariable calculus
* Along with that one may also need to translate math into a
form of working code which means that one needs to have a good deal of
sophisticated computing skills
* This article is an introduction which would help someone in
understanding of the mechanics of machine learning and thereafter describe how
to translate math basics into usable code
* If one would like to apply the existing machine learning
knowledge for implementation of practical purposes and practical projects ,
then one can leverage the best of possibilities of machine learning over
datasets using R language and Python language's software libraries using some
basic knowledge of math , statistics and programming as Machine learning's core
foundation is built upon skills in all of these languages
* Some of the things that can be accomplished with a clearer
understanding and grasp over these languages is the following :
1) Performance of Machine Learning experiments using R and
Python language
2) Knowledge upon Vectors , Variables and Matrices
3) Usage of Descriptive Statistics techniques
4) Knowledge of statistical methods like Mean , Median , Mode ,
Standard Deviation and other important parameters for judging or evaluating a
model
5) Understanding the capabilities and methods in which Machine
Learning could be put to work which would help in making better predictions etc
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