What is Machine learning and why bother learning it?

Image: DCBA robot

The artificial intelligence is a major player in technological changes and developments. Although some of us might not notice, it is powering many gadgets, apps, and software developed in the last years.

One of its subsets that gained too much popularity in the last years is Machine learning.

A simple way of understanding Machine learning is by seeing it as the usage of statistics and data to find patterns and automate computer learning processes to make better future decisions.

The process of teaching a computer starts by collecting and observing huge sets of data. They generally include words, images, audio, numbers, clicks and anything else that can be stored digitally.

All major services we have access to these days are powered by ML. From recommendation systems such as Netflix, YouTube, SoundCloud; Search engines, voice assistants and social-media platforms such as Facebook, Twitter, and Tik Tok.

All these platforms keep a record about your past activities and preferences – what you watched, what you clicked, which interests you have, how long you stay on the page, what you react or comment to, etc. and use this information to determine patterns and choose what they will provide you the next time you return to their pages or apps.

Today machine learning is powering and changing many industries, not only famous tech startups but also banking systems, insurance, media and publishing, oil and gas, public administration, transportation and logistics companies from around the globe.

Types of machine learning

Machine learning can be subdivided into 3 main categories, each one of them relying on particular tools, techniques, and algorithms.

Supervised learning

It is the most popular form of machine learning. In supervised learning, the machine is told exactly which patterns should be looking for.

Before the machine starts looking for patterns by itself, it’s created a dataset whose role is to train the model or the machine. Once the model is trained the machine can start to make decisions by itself every time new information is added.

Unsupervised learning

In unsupervised learning, the machine uses information that is neither classified nor labeled, and from that point on it tries to figure out the output. In this technique, the machine is not instructed on how to think, but it figures out things by identifying patterns and relationships in the dataset provided. It then creates clusters, separating each group based on their characteristics. For example, if a machine is given images of cats and dogs, it is going to learn how to sort them based on height, the shape of their eyes, ears, tails, etc., then it is going to create clusters to properly sort and automatically identify these groups in future.

Reinforcement learning

In reinforcement method, the machine interacts with its environment to find out what’s the best outcome. In this category, the machine is given a method to learn by trial and error and is rewarded every time it solves the problem or penalized if it fails.

Deep learning

Deep learning is a more advanced form of machine learning, that makes use of neural networks to enable machines to make more accurate decisions without help from humans.

A deep learning model is programmed to analyze data using the same logic behind the human brain. To train deep learning models we can use both supervised and unsupervised learning.

Deep learning is important to perform complex predictions or tasks. To perform them, deep learning applications rely on a neural network structure also called artificial neural network or deep neural network.

A deep neural network is made of multiple layers similar to the human brain. The first layer is called an input layer, the last is called the output layer. Between these two layers, we find hidden layers composed of neurons connected to each other and each layer.

Image: Example of a neural network

Image 1: Example of deep learning input and output layers 1. Source: O'Reilly

Deep learning is the technology powering autonomous vehicles, image and text recognition tools, news aggregators, language recognition, text-to-speech apps, and many more.

To have reliable results while training deep learning models, we need huge amounts of data and power to process the algorithms. The first one is easy to fuel considering some insights2 that show that:

  • On Google alone, more than 1 trillion of searches are made yearly;
  • People share more than 100 terabytes of data daily and send 31 million messages every minute on Facebook;
  • Only this year (2020) more than 1.4 billion devices will be shipped worldwide and every human on the planet will produce 1.7 megabytes of information each second.

These days many companies offer the CPU’s, GPU’s and other devices needed to process these data, all left is the existence of a stable power grid or alternative ways of generating (clean) electricity to boost the mining of big data.

Why should you learn machine learning?

Machine learning is helping many companies globally to solve complicated problems and create new solutions. As a result, many jobs are being left behind and also created. Here are a few reasons why you should learn machine learning:

  1. Job numbers in Machine learning are growing fast;
  2. The salary of machine learning engineers is very high;
  3. It helps you understand your customer better;
  4. Helps you detect frauds and save money;

Final notes

We simplified the definition of machine learning to any person considering to learn or work in this field, or even not. There are many types and tools to learn this technology. So, before picking up one, determine your goals and expected outcomes.

Footnotes

  1. In this example, a trained algorithm could recognize a “beach city” using a simple image provided to its dataset. Read more here.
  2. See: Hosting tribunal big data stats

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