A simple explanation of Machine Learning
Machine learning is a type of artificial intelligence(A.I.) that provides computers with the ability to learn without being explicitly programmed. More specifically, machines use a set of input data and observations and attempt to extract useful information and conclusions from that set using intelligent algorithms. This process is called training. A machine that is able to learn changes its outcome and improves its performance when exposed to new data.
The goal of artificial intelligence is to create a machine which can mimic a human mind. Machine learning aids in this goal by giving computers the ability to learn from past experience, just like humans do in everyday life. To achieve this, machines combine data mining with statistical models to reach to certain conclusions.
Types of Machine Learning
Machine learning is separated into three categories: Supervised, unsupervised and reinforcement
Supervised learning is where you train the machine using data which was previously labeled. This means that you feed the machine with information where the outcome is already known. This process teaches the machine more about a specific subject, helping it understand how it behaves. The greater the dataset the more the machine can learn about the subject matter.After the machine is trained, it is given new, previously unseen data, and the learning algorithm then uses the past experience to give a result.
Unsupervised learning is where the machine is trained using data which wasn’t previously labeled. The machine doesn’t know what the data is about. It is only given information about the subject without naming it. Machine learning algorithms analyze the input data, trying to form a model about the subject matter. The machine tries to recognize patterns that will help it understand what all the information is about. The main advantage of unsupervised learning is that once the unlabeled data has been processed it only takes a small set of labelled data to make the learning effective.In general, there are huge amounts of unlabeled data and only small amounts of labeled data, making unsupervised learning even more valuable.
Reinforcement learning is similar to unsupervised training in that the training data is unlabeled, however when asked a question about the data the outcome will be graded. More specifically, the machine in this case operates in a trial and error manner. If the outcome is negative then a “bad grade” is given, which tells the machine to avoid repeating the same error again. A good example of this is playing games. If the machine loses, it breaks down the game to a collection of moves trying to understand what went wrong. Eventually, the machine will learn not to repeat the same mistakes again and constantly improve over time.
Machine learning has begun changing our world and it is clear that it’s going to be at the center of cutting edge technological innovations in the following years. Technology giants like Google and Facebook, have already begun integrating machine learning to their products.
If you’re interested in learning more about artificial intelligence, we are going to create a series of articles surrounding the field of A.I.This series will include topics such as: Deep Learning and Neural Networks, Self-aware machines, Emotion detection, Robotics and more.
You can find the link to this series here: Exploring Artificial Intelligence