Normal Equation

In this article I want to make a brief introduction to normal equation method. The article is following the Coursera structure, summarizing the following aspects: some theoretical points two practical examples general case advantages and disadvantages between gradient descent and normal equation Advertisements

Tagged with: , , , , , , ,
Posted in Coursera

R Ladies Coding Club

It is clear for everyone that even if nowadays it isn’t about discrimination on sex, there are more guys in programming field than girls. Anyway, this is not a posting about girls in IT or programming field, this is about

Tagged with: , , ,
Posted in Conferences

Features and Polynomial Regression

Houses prices prediction Let’s say we want to forecast the price of the house. So, we have two features – the frontage of the house and the depth of the house.

Tagged with: , , , , ,
Posted in Coursera

Data Science for Game Analytics

On Tuesday, July 26, I attended for the first time a meet up organised by Data Science Fest. The location was awesome, actually, I think without having many doubts, it’s the most amazing place I’ve ever seen: King empire. More details

Tagged with: , , , , , , , ,
Posted in Conferences

Gradient descent for linear regression (1)

A very common method used for defining a model and estimating its parameters, it is to minimize model’s errors with Gradient Descend. The Gradient Descent estimates the weights of the model in many iterations by minimizing a cost function at every step.

Tagged with: , , ,
Posted in Coursera

Feature scaling

According to Wiki, feature scaling is a method used to standardize the range of independent variables or features data. In data processing, it is also known as data normalization and is generally performed during the data preprocessing step.

Tagged with: , , , , ,
Posted in UsefulStuff

Errors vs. Residuals

I know, for many of us, when we first encounter these two terms it is a bit hard to understand the difference, especially because sometimes both unobserved errors, but also residuals are simply called “errors”.

Posted in UsefulStuff