What is machine learning?

Machine learning is a concept that allows the computer or machine to learn from experiences and examples even without any particular program.

In machine learning, you do feed data in and generic algorithm. There is no need to design or write the program, but algorithms behave based on given data.

Types of machine learning:

The focus of machine learning is learning. There are three different types of machine learning mainly. In this blog, ill discuss these three in detail.

As with every method, there is different technique and method to deal with the machine algorithm. Every process has advantages and disadvantages.

To have better insight, we have to look at the data that the method ingest. In machine learning, there are two kinds of data- labeled data and unlabeled data.

Labeled data has both input and output parameters incomplete machine-readable format, but it requires many laborers to label this data.

But in non-labeled data has one or, in some cases, none of the parameters is machine-readable. It also decreases the requirements of laborers, but it needs a more complex solution.

There are many types of machine learning, but ill discuss three types of machine learning used nowadays.


01. Supervised learning:

One of the primary learning methods of machine learning is supervised learning. In this type of data, the machine is structured based on Labeled data. Supervised learning is power when used in the right direction. Also, data need to be labeled accurately for this method to work.

In supervised learning, the machine learning algorithm is given a small dataset for training. Then algorithm finds the relationship between the given parameters and then fine the causes effects between the variables in the dataset.

At the end of the training, the algorithm has an idea of how this data works, also the relationship between the input and output. Supervised learning is the first of three types of machine learning.

02. Unsupervised learning:

Unsupervised machine learning, which works with unlabeled data. It means that humans labor is not required in this machine learning, allowing the much larger dataset to work correctly.

In unsupervised, machine learning does not have the label points of work. The relationship between data points is perceived by algorithm, no need for human assistance to any input. it is also one of three types of machine learning.

Unsupervised learning data is being versatile because of the hidden structures of the algorithm. This learning algorithm can adapt the information dynamically by changing systems.


03. Reinforcement Learning:

Reinforcement machine learning is inspired by human beings, how they learn from theirs lives, And trial and error. Favorable outputs are reinforced or encouraged, and unfavorable outcomes are punished or discourage.

In the program’s case finding the correct solution, the algorithm reinforces the solution by providing an algorithm’s reward. But if the outcome is not favorable, the algorithm is forced to run until it gets better results.

In a typical reinforcement machine learning case, such as finding two points on the map, the solution is not absolute. More and more rewards will be given if the percentage is higher.

In this way, the program is designed to give the best possible solution for the best rewards. Reinforcement machine learning is one of the three types of machine learning.

Applications of machine learning:

Machine algorithm Is now used for the substitute of middle-skilled labor. For example, in large companies B2C the customer service is being replaced by natural language processing machine learning, also known as chatbots.

These chatbots work on Artificial intelligence and provide support to the human customer support and deal with the customer directly.

Three types of machine learning have improved our experience a lot, especially on social media platforms. Online or digital platforms like Facebook, Netflix, Amazon, and Google all provide the data based on the user’s likes and dislike.

Facebook uses the recommendation engine on the news feed fr both Instagram and Facebook. It also uses the user data and then applies its machine learning to show relevant ads to the user depending on users’ searches.

Google uses its machine learning for the google news recommendation and the recommendation of the video on youtube.

Amazon also uses machine learning and place the related and best match products on the user view. This activity increases the conversion rate to its maximum peak.


Machine learning is now using every field to improve the user experience. Most of the online platforms use this machine learning to increase the sale and conversions. So this is now important for every field.


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