It’s a lazy Sunday and you are scrolling on your Facebook. You have recently finished watching Stranger Things Season 4. As you are obsessed with it so you have joined many of its fan groups. Now all of your Facebook feed is taken over by suggested posts about Stranger Things. Do you know how? The reason is Machine Learning. Let’s move forward and talk about it in more detail.
What Is Machine Learning?
With the Stranger Things example, you must now have some idea about Machine Learning. Machine Learning is a branch of Artificial Intelligence in which a machine ‘experiences’ and ‘learns’ something. In machine learning, we feed historical data to a machine so it can understand the data and make predictions for future data.
How Does Machine Learning Work?
The historical data that we feed to a machine is divided into two sets. One set is called as ‘training data’, while the other is called as ‘testing data’. First, we ‘train’ the machine on training data. The machine learns its patterns, trends and other insights. Then, we ‘test’ its learning using test data. Based on its learning, the machine makes predictions for test data. We compare these predictions with actual results in the test data to see how accurate our machine predicts. We try again if the predictions are not accurate enough.
What Are the Types of Machine Learning?
There are four types of Machine Learning –Supervised, Unsupervised, Semi-supervised, and Reinforcement learning.
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Supervised Learning
This is the simplest type of machine learning in which training data is well-labeled and structured. Input and Output variables are defined. Once this data is fed to the machine, it already knows which are input and output variables. It only has to learn patterns and trends that exist in this structured data.
Unsupervised Learning
In this type of machine learning, the training data is not labeled. Input and Output variables are not defined. The machine tries to find trends and patterns in this unstructured, unlabeled data to learn about the response or output variable.
Semi-supervised Learning
This type of learning involves both Supervised Learning and Unsupervised Learning. A labeled training data is fed to the machine but the machine is free to understand this data on its own. It finds hidden trends and patterns which are not affected by how the data is labeled and which are Input and Output Variables. This is more complex than the other two.
Reinforcement Learning
This type is the most advanced form of machine learning. Ever seen a robot? That is an example of Reinforcement Learning. In this type of learning, the machine learns by experience. It is feedback learning and a multi-step process. The machine experiences something by performing an action and gets either a positive or negative response as feedback. The machine has to perform the action again and again until it receives only positive feedback in response.
Machine Learning is all around us in this era of technology –even your virtual assistants like Alexa and Siri are an application of Machine Learning. Don’t worry, you can find more detail about these topics in our blog section. Happy Reading and Learning!
