Ever wondered how YouTube, Netflix or even Amazon gives you different suggestions that actually interest you? Two awesome words: MACHINE LEARNING. In this article we will uncover the Machine Learning Basics which greatly impact the science of Artificial Intelligence.
But that’s just the tip of the iceberg when it comes to Machine Learning. Machine Learning covers a whole lot more than we can ever imagine:
As part of diving into the Machine Learning Basics, we will also unravel the parts that are responsible for keeping Machine learning systems up and running. These major parts are namely Model, Parameters, and Learner.
Parts of Machine Learning System
Before we dive deeper into this matter, let us first be familiar with a few basic terms and parts of a Machine Learning System.
The Model is the system that is responsible in making predictions and identifications. Parameters are the signals and factors utilized by the said models in order for it to form its decision. And lastly, the Learner looks at differences in predictions versus the actual income.
With these three in place and working accordingly, Machine Learning is more feasible and attainable.
Popular Machine Learning Techniques
Supervised Learning and Unsupervised learning: these are the two of the most widely adopted machine learning methods in Artificial Intelligence. Supervised Learning comprises 30% while unsupervised learning accounts for only 10 to 20 percent.
In Supervised Learning, the algorithm is already aware of the output before it even sees or starts analyzing incoming data. This is what we can refer to as labeled data. This labeled data is then compared to whatever data you will start feeding into the neural net. This only means that the aim in supervised learning is to find error in an existing data using the correct outputs already in place.
Moreover, it utilizes methods like classification, regression, prediction and gradient boosting in order to predict a data based on the input it has been given. Supervised Learning is most often used when data in the past are used for future events.
Let’s take a jigsaw puzzle for example. Every box of jigsaw puzzles comes with a picture of the finished puzzle, right? You’ll see this even before you start putting it together. This gives you the idea of what the outcome will be once you’ve put each puzzle piece in its proper place and with the help of the finished puzzle in place it makes it easier for you to fix the puzzle pieces together.
Unsupervised Learning however, uses a different algorithm in interpreting data. Unlike supervised learning, it uses an algorithm which doesn’t utilize any no known data for comparison. The algorithm’s main aim is to analyze what is being shown. It has to explore the data further and conceptualize the structure within.
Imagine being given 1000 puzzle pieces with no guide whatsoever on what the finished product will be. Is it a plane? A house? Mona lisa? You wouldn’t know. Unless you start analyzing the pieces and try fitting them altogether.This is Unsupervised Learning.
Common Machine Learning Algorithms
Another essential part of the Machine Learning Basics is algorithms. As we know, these algorithms make it possible for machines to actively interpret, learn and predict data. An algorithm is a process or set of rules to be followed in calculations or other problem-solving operations, especially by a computer.
Simply put, algorithms are our way of communicating to machines in order to tell them what to do. The difference between this way of communication and our interactions as humans is that, sometimes you only need to play between two different numbers.
With a field as wide as Artificial Intelligence, there are multitudes of algorithms which can be used for Machine Learning. But to save you the trouble of digging through them all, we’ve narrowed them down for you:
May it be by simply browsing through your Netflix suggestions or opening your automatically sorted e-mails, we cannot deny that Machine Learning is everywhere. Though decades of researching and perfecting an algorithm that can match human intelligence have been made, we have much more to unlock when it comes to Machine Learning. We have seen how much it has made marvelous through the 59 years it has rose into our machine. Now, what more if we add another 59? Tune in as we dig more into its concepts and provide a more detailed experience which encompasses what we currently know about Machine Learning.
Fasten your seat belts, this isn’t the last we’ve will hear of Machine Learning.