Since the time artificial intelligence has been introduced in the world, it has fascinated and astounded people every time. Various movies have depicted the applications of artificial intelligence in our day to day lives. There are many aspects of artificial intelligence. One of those is machine learning. In this article, we will get to know about machine learning and its various types.
What is machine learning?
It is a field of study where the machine is capable of analyzing its performances and changes its way of doing the task with betterment. There is a minimum role for human intervention to improve the working of a machine. The name itself tells that machine learns by performing any task and later utilizes those learnings to perform other tasks efficiently.
What are the types of machine learning?
The most widely used machine learning methods are supervised and unsupervised learning. Apart from these methods, there are two other methods. All of those are mentioned below.
It is one of the most commonly used branches of machine learning. It uses data that has predefined input and output. The example is analyzed by the learning algorithms. The algorithms understand the patterns which change the input into an output.
Then a new set of data is fed into the machine and it decides the output by using the same pattern as it used previously for supervised data. To put it simply we can understand it like this:
Suppose you have a bag containing square balls and spherical balls. Now the first step is to train the algorithm of the machine with different balls in this manner
- If there are edges on the balls then it will be labeled as –square balls
- If there are no edges on the balls then it will be labeled as – spherical balls
Now, after training the algorithms, when a new ball having edges is presented into the machine. The algorithms of the machine classify the ball with its edges and put it in the square-shaped ball category. In this way, a supervised machine learns from training data and then applies it to check the new data.
Another Example of Supervised machine learning
Another example of supervised learning is emails. We daily use emails but never question how does it categorizes the mails into junk or into a regular folder.
Before starting to classify emails, the machine is trained with many pre-labeled emails as either junk or regular.
The machine learns the various patterns and attributes of junk and regular emails. It then takes millions of emails daily to check similar patterns and attributes to classify them into junk or regular emails.
There are many uses of supervised machine learning but it has some downsides too. It can perform only those tasks for which it has been trained. As it keeps on performing those tasks it gets more efficient in it.
As the name suggests, in this type of learning the machine doesn’t get any prelabeled data. The machine has to analyze the data and act in accordance without any supervisor. The machine classifies the data based on its similarities, patterns, and differences.
Thus, the machine finds the hidden structures in unlabeled data by itself. For example, an image has both a lion and an elephant, which is never seen by the machine. Therefore, the machine doesn’t know about the features, patterns, and attributes of a lion and elephant. However, it can classify them based on their similarities and differences. The first part of the classification may have all the photos having lions and the second part may have elephants in it.
Here machine didn’t learn anything previously unlike in supervised learning.
It is used in places where supervised learning cannot work. Semi-supervised learning uses both labeled as well as unlabeled data for training. Generally, a small amount of labeled data and a large amount of unlabeled data is used in this type of learning.
Semisupervised learning plays a crucial role when the cost of labeling is associated with the training process. The most common example of this learning is identifying a human’s face on a webcam.
In this learning, the algorithms find the most suitable way with trial and error method to maximize the reward in a given situation. It is different than the supervised learning where the machine already had an answer key to learn the patterns and attributes related to it. But, in reinforcement learning, there is no prelabeled data and its algorithms decide a way to maximize the reward.
For instance, suppose there are multiple paths to go from point A to B. Also, there are hurdles in some paths. A person starts from A and moves towards B. He will go through different paths and learn about the hurdles. Then he will choose the best paths with the least hurdles to reach point B.