# Discrete Mathematics #02 | Set Equality

In the previous lecture, we talked about sets as structure. This article will go a step further and talk about Venn diagrams and a simple set relationship which is set equality. In other words, when do two sets are equal. To make the equality of sets a reality, we must speak of the Axiom of Εxtensions and Venn Diagrams.

A Venn diagram is an iconographic representation of sets, popularized by John Venn in the 1880s. It is a diagram that shows all possible logical relations between a finite collection of different sets. Let's assume that we have a set A…

# Discrete Mathematics #01 | Sets

This series gives the reader a flavour of discrete mathematics and its applications to the computing sector. My goal is to learn and share a mathematical field with applications to cryptography, coding theory, formal methods, language theory, computability, artificial intelligence, theory of databases, and software reliability.

Discrete mathematics is a mathematical structure that is fundamentally discrete rather than continuous. To simplify the previous sentence, a discrete object is something that we can enumerate by integers. Discrete structures can be finite or infinite. For example, a collection of numbers represented by a graph is countable.

In contrast with discrete mathematics is…

# Introduction to ML — History

Machine learning, aka ML, is living the third period of recognition. Almost any company, regardless of its size, uses machine learning to process the data and aggregates them in a way that lets us make predictions.

These predictions can be applied in many fields. Questions such as what will happen in the stock market or the weather or the prediction of a robot movement after an action, can be answered due to machine learning.

# Gradient Descent, Normal Equation, and the Math Story.

In a world where data is becoming more valuable than gold, machine learning is trying to use these data for marketing, customer satisfaction, problem-solving and many many other reasons. However, the question arises, how we extract the maximum possible value from a given dataset? This is a question that I will attempt to tackle in this article.

`# Import the required libraries## import pandasimport pandas as pd# import numpyimport numpy as npnp.seterr(all='warn')# import matplotlib for visualization import matplotlib.pyplot as plt# Read your collected datadata = pd.read_csv("car_pricing.csv") …`

# Learn the Theory of Linear Regression With Python Implementation

## What is a linear regression?

Linear regression is a statistical procedure for finding the relationship between two (or more) continuous quantitative variables. For example, a real estate agent knows that the square footage of the house is related to the price of the property. Machine learning embraced this idea and used it to predict an unknown quantity (called a dependent variable) from known quantities of another variable (called an independent or predictor variable). That means that if we know the square footage of a house, we can predict the cost of it.

When it comes to relationships, there are three types. We have already discussed… ## Evangelos Patsourakos

Computer science became my passion since I entered university. Programming always keeps me motivated because of the fact that it allows me to improve our lives.