Bernard Joseph Jean Bruno/Getting started with Machine Learning part 1: Introduction

Created Wed, 26 Sep 2018 17:04:50 +0000 Modified Thu, 21 Jan 2021 21:30:48 +0000
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“Getting started with Machine Learning part 1: Introduction”

“Getting started with Machine Learning part 1: Introduction”


DATA… The holy grail of the 21st century! Mostly valuable for businesses nowadays. Why should we concerned about this?

Data is just a chunk of information which describes something or someone. In the Machine Learning world, we need to fetch as much data to make a general estimation of something. Simple example; in tech-businesses, they fetch data from users with which they can make targeted advertising or “suitable” products for you.

The most common example we can take is NETFLIX: the plethora of movies and series which are accessible by everyone (of course after buying a subscription) but if you have tested the platform you’ve noticed that it automatically make a proposition of movies and series, based on what you’ve recently watched.

Now based on what I’ve just told you, let go deep into what’s Machine Learning. First, there is no such thing as you put a book in front of your computer and it starts to learn things on its own (well, maybe in a near future…). But for now, what you can do, is to make that illusion of making your computer to learn.

The basics needed


What a computer can understand are 1’s and 0’s; mathematics, physics and engineering were involved to make a computer understand what we want to tell the computer to do, this is called PROGRAMMING!


Houston! Are you still with me? Well, first, to learn machine learning you should be acquainted with programming, at least the basics. But the foremost part of machine learning is MATHEMATICS! For my coming blogs with the maths part, I will keep it as simple as possible so anyone with no maths background can understand it.

Application of Machine Learning

We can use machine learning for automatic car driving, filter mail spam, recognize something or someone in a picture and recommending music, videos to your targeted audience. Where it has been used; Google Car, Google Search, Your mailbox, youtube, LinkedIn, Facebook among many things. Next, will be you!

On the next blog, we will talk about a theorem called** Bayes ***found in the probability field*, and how we can make a simple “Machine Learning” algorithm to **classify some data**.

See you on the next blog with paper and pens with you, I have some simple calculations! Always keep thinking!

nerd dance.gif

Nerd dance! ha!

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