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EXPLORING MACHINE LEARNING –

Today in the era of increasing technology, we can see how fast the machine is learning and developing itself. The machine learning is the concept of artificial intelligence that provides the device to learn itself new things without the help of human just by observing the environment. IT focuses on the development of a program by itself and without the use of any other resources.

Now we see how the process of machine learning begins with –

The process begins with the observations of different types of data, representations of different kinds of patterns in data. The primary ai of machine learning is to make better decisions by the comments with itself.

Let us see the difference between machine learning and artificial intelligence –

Artificial intelligence and machine learning both are the parts of computer science, and both of them correlate with each other. These are the two technologies that are used in creating the intelligence system.

Artificial intelligence is a technology using which we can create an intelligence system that simulates human intelligence while this is a subfield of artificial intelligence that enables the machine to learn from the experience and perform without being exility programmed.

The artificial intelligence is classified into three parts –

Weak AI

GENERAL AI

STRONG AI

Explore Machine Learning

The machine learning is divided into three parts –

Supervised learning

Reinforcement learning

Unsupervised learning

Let us see these types in brief and understand them –

  1. Supervised learning :- It is the most common super branch of Machine learning. These are the algorithms that are learned by examples. Now, as the name “supervised” originates from the idea that in this type of algorithms, there would be teacher supervised in the whole process, when training supervised learning algorithms, the data will consist of input paired with correct outputs.
  2. Reinforcement learning :- In reinforcement learning, there is no answer, but a reinforcement agent decides to perform the given task. In the absence of a training dataset, it is bound to learn from the experience. Reinforcement learning differs from both unsupervised and supervised learning.
  3. Unsupervised learning :– In this kind of machine learning, it infers the pattern from a dataset without reference to known. It can not be directly classified as supervised learning because they don’t have any idea of what the values for the output data might be. They are used to discover the underlying structure of data.
Explore Machine Learning

We have seen the types & now let us see some of the common areas where it can be used: –

  1. Virtual assistant – we have seen many virtual assistants like Siri, Alexa, google assistant, and many more. We wake up using these assistants and use the whole day. These are just built with the help of machine learning.
  2. Predictions while commuting – We all have been using GPS service on our mobile. Now the work GPS does is it calculates the current location and velocities, and these are saved to the central server of managing traffic. Then this has been used to build the maps of existing traffic. All this work is done by machine learning.
  3. Social media services – most of us start our day will social media. From personalizing the newsfeed to target the ads, all the work is done by machine learning. Big social media companies’ analysis the use of apps and then apply the algorithms.
  4. Face recognition- As we see that face recognition is seen in every smartphone. The use of this is there in the process. The AI uses our face dots and verify it and thus how this works.

There are millions of areas where you can see the use of machine learning. With the increase in technology day today, the use of machine learning will also increase. But if we exceed the limit of using this, then it will be dangerous to us.

I hope this blog helped you. Do tell me in comments about what could be added in this blog also share this blog if you learned something.

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