**Before you go further**

Event in probability is just a non-deterministic output of some random experiment. Tossing a coin is a random experiment whose output is an event and it cannot be determined before tossing it. Therefore, it is a non-deterministic machine experiment. Generating a random number using a code in computer is deterministic because it uses some algorithm behind curtains whose output can be determined using certain practices. Tossing a coin never uses an algorithm behind it. You just flip it and get output.

**Dependent and Independent events**

Output of a dependent event depend on some other event. For…

**Introduction**

Managing your software is always more important than developing it. This requires following thousands of coding standards and handling hundreds of vulnerabilities and edge cases. One of the features of programming languages that helps you in your software development lifecycle is assertions. Assertions are logical functions to check whether particular requirement is met before code runs further. If that condition is not met, the program terminates and gives assertion error and doesn’t run further lines of code.

**Let’s dive more into it**

You wrote a code and assign a variable, let’s say *x* as 5. After few lines of…

- What is feature engineering and dimensionality reduction
- Variance and Central Tendency (Mean)
- Covariance
- How can you use this in Machine Learning
- Conclusion

Feature engineering is probably one of the most crucial step that you need to take before processing the input matrices of features in Machine Learning model. Feature engineering aims at figuring out the relations and properties of different random variables or features in dataset. Random variable is simply the set of outcomes of non-deterministic machine or random experiment(like throwing a dice). This can likely help us in removing the features that do not contribute in target variable. This…

There exist hundreds of applications where you need to find the length or magnitude of a certain vector whether it being a school physics problem statement, a real life scenario or even the Machine Learning itself. Magnitude of vectors have solved many problems, especially in Machine Learning that have helped advance the development of models. I will discuss one such application in this article itself, which is regularization. Let’s dive into the norms of vectors.

Norm is nothing but the magnitude or length of a vector in a vector space. …

Object oriented programming or so called oops, is a paradigm of programming languages that deals with objects being created under particular class consisting of some functions or methods that can be performed for every object passed into class.

I know this was a bit technical explanation though of this concept. Let’s dive into what actually they mean, when they say “**oops**”. Let’s say, you got a pressure gauge to measure tyre pressure of an Audi car. You note down the pressure, you bring another pressure gauge, you measure the pressure of Lamborghini this time and bring a new gauge for…

When we hear the word 'matrix' , it pretty much draws an intuition of a rectangular array of numbers or that’s what the books call it.

When studying the chapter of Matrix in school mathematics, who might have guessed that this rectangular representation of numbers is what AI is hunting for. For better intuition of how a matrix deals with Machine Learning, let’s say you have 'm' number of features of an object that you want your Machine Learning algorithm to look, for classifying that object as either 'A' or 'B’. If we represent these features as a column vector…