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[softwareguidance]

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[srimanit]

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[tekshyam]

#21 Practical Python Dictionaries Part 2 | Python Tutorial for Beginners...

[kunal4511]

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.

 Note: Start your journey in Data Science with Learnbay as it provides big data analytics training and Data Science Courses In Bangalore.

 Mathematics involved in Machine Learning:

Calculus

Linear Algebra

Probability

Statistics

Optimization

Complexity

Now let us see why and where these are applied in Machine Learning.

 Linear Algebra - 

 Stated as “The Mathematics of the 21st century” and as “The Mathematics of Data” is literally everywhere in the field. It is a continuous form of Mathematics that is used

To read and write vector and matrix notations

Its notations are used in describing operations on data precisely with specific operations.

To read and interpret statistics

To describe tools and techniques of statistical methods.

Calculus -

 Calculus is very popularly known to be used in not only Machine Learning but also in electricity, harmonics, acoustics, astronomy and dynamics. But Multivariable calculus is what is majorly used in Machine Learning to create mathematical models to arrive into the optimal solution, in building Machine Learning models and probability

 Also known to be Bedrock of Machine Learning, is a very much important aspect of the field as it is used in decision making algorithms in machines.

 Many algorithms will be designed using probability lines like Naive Bayes.

Sub-fields of study are built on using Bayesian networks of probability.

Other than this there are many others to be studied by an aspiring ML engineer, Learnbay could help you to achieve it as it provides a nicely structured Machine Learning course. You can know more about the course here.

 I recommend you the data science course of Learnbay, it is really a good source to learn data science as it provides big data analytics training and Data Science Courses In Bangalore.

[tekshyam]

#20 Practical Python Dictionaries | Python Tutorial for Beginners Tek So...