Machine Learning, Types and Examples.

    Machine learning is a small part of Artificial Intelligence, in today’s time it is one of the most trending technologies in the world. From Google to Facebook, all the big tech companies use it.

    What is Machine Learning?

    It is part of Artificial Intelligence. It is used to teach the machine and the machine is also taught how it can take decisions using its past experience when needed.

    The main purpose of it is to make computer programs (Human Intervention) advanced without any human intervention. In this, the machine is made so efficient or smart for a particular task, that the machine can complete that task on its own and continuously improve it on the basis of its previous experience from the next time. That is, no coding is used explicitly by the programmer, but by understanding the things or work, the machine automatically creates its pattern, and completes the work accordingly.

    The ML system or program that is trained is called Machine Learning Model. Machine learning model is a computer program, it takes input and then learns from experience and predicts output.

    Pattern, Prediction, Input and Past Experience are necessary to teach any machine. Using all these, the machine is made such that they can automatically take the decision itself (no human is used to take the decision) and can give output accordingly.

    Types of Machine Learning

    There are three types of Machine Learning, Supervised Learning, Unsupervised Learning and Reinforcement Learning-

    • Supervised Learning In Supervised Learning, Input and Output data is already provided to the machine, which is also called Training Data or Labeled Data. According to this data, the machine gives its output and the output completely depends on the quality of the training data, that is, if the quality of the data is good, then the output of the machine will also be better. When a new input is given to the machine, the machine will give output only according to its previous experience and data.
    • Unsupervised Learning In Unsupervised Learning, the machine is not given any input and output labeled data in advance. In this, as soon as the machine gets any input, the machine itself calculates it and prepares a cluster and divides them into different groups according to the type of things.
    • Reinforcement Learning : As in Supervised Learning, the training data and output are already labeled with the machine. On the contrary, in Reinforcement Learning, the machine does not have any answer and due to no training data, the decision is taken by the Reinforcement Agent to complete the task here, who try to complete the task on the basis of his experience. and learns from his repeated efforts.

    Difference between Machine and Traditional Programming

    1. Traditional Programming: Here we feed DATA (Input) + PROGRAM (logic) in the machine, to run the machine and finally we get the output according to our data and program.
    2. Machine Learning At the same time, here we feed DATA(Input) + Output into the machine, and on running it, the machine develops its own program (logic) during training, which can be later evaluated during testing.

    Examples of Machine Learning

    There are many examples, out of which you can take the example of Facebook because everyone uses Facebook. While using Facebook, you must have noticed that often the profiles we check, or share something in other groups, automatically Facebook starts giving us notification, that you know them, or can include them in Friend List. Huh.

    Another example you can take from Netflix, where based on the movies you have watched or liked, you start seeing many other similar movies, that is, the pattern of your previous search records is prepared from Machine Learning here, and the corresponding data is presented to you.


    There are many applications of Machine Learnings in our daily life, which we all use, some of which are as follows.

    • Facebook :- Facebook is used in large quantities all over the world and we all use it. Machine learning is used in Facebook’s Automatic Friend Tagging Suggestion, in which based on Face Detection and Image Recognition, Facebook checks in its database and recognizes a photo or image.
    • Shopping Websites If you shop online, then you must have noticed that the information related to your searched product starts appearing everywhere. Like you did some search on Amazon and after some time when you open Facebook or YouTube, you start seeing science related to the same product there too. So all this is the wonder of Machine Learning, in which Google takes care of your every activity, and shows you ads accordingly.
    • Uber :- If you use Uber for traffic, then you must have seen how Uber itself detects the location of the customer, the actual location of the vehicle is also visible in real time, the shortest and open routes to the driver Also, Uber keeps on altering its Charges when there is a huge demand, so all this is possible only through Machine Learning.


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