Saturday, 31 October 2020

Mathematics in Data Science

Even though mathematics is different from a field from Machine Learning, it still is greatly involved in the layers and inches of it. We know that logical reasoning is a skill that should be mandatorily possessed by engineers or programmers but factually, specific areas of mathematics are highly important in Machine Learning and those are highly necessary to be learnt by Machine Learning engineers. Read on to know what areas they are and why they are so important.

In a demographic of the mathematical knowledge you need to know to learn Machine Learning. 60% of Mathematics in ML is concerned with Probability, Statistics and Linear Algebra. Algorithms and Complexity is something that you would have learnt in your undergraduate (if you have a CS background) and Multivariate Calculus comes into picture when you deal with a lot of features and very huge data.

Start with Linear Algebra at the Kindergarten level and then focus on basic Probability which covers all Bayesian methods and then move on to Statistics. Once you cover the basics then comes the next biggest thing in ML. Applying the Basics!

All ML algorithms have libraries that make them run in seconds but you don’t know the underlying mathematics that’s happening in the algorithm. Try to implement basic mathematics by coding a ML algorithm from scratch. execute and see line by line to derive the basic mathematical process behind an algorithm. After doing this, you go for core Linear Algebra, Applied Probability(Descriptive and Predictive Modelling) and Applied Statistics.

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