# 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.