Introduction To Machine Learning

1)WHAT EXACTLY IS MACHINE LEARNING?
                   


MACHINE LEARNING ,Now the fastest growing technology around the world.

why one should learn machine learning is ,To settle in your life Quickly and to get highest paid salary . The future will be Machine Learning . There is  no doubt in this.

So, what Exactly the Machine Learning is?



In my opinion ML is writing a particular Piece of code which gives intelligence to the machine(In this case Our Computer). It is seen as a subset of Artificial Intelligence.

Mathematics likes statistics and probability are essential for deeper understanding of machine learning



Machine Learning is used to make predictions and classifications which makes Humans life more simpler!!

Weather prediction
classification

Predictons are like 'House-prices in particular area' , 'Predicting RainFall ' etc..

Classifications are like ' spam and not spam mails ', 'classification between two or more objects' etc..

Applications

There are many applications for machine learning, including:


Machine learning approaches[edit]

Early classifications for machine learning approaches sometimes divided them into three broad categories, depending on the nature of the "signal" or "feedback" available to the learning system. These were:
Supervised learning: The computer is presented with example inputs and their desired outputs, given by a "teacher", and the goal is to learn a general rule that maps inputs to outputs.
Unsupervised learning: No labels are given to the learning algorithm, leaving it on its own to find structure in its input. Unsupervised learning can be a goal in itself (discovering hidden patterns in data) or a means towards an end (feature learning).
Reinforcement learning: A computer program interacts with a dynamic environment in which it must perform a certain goal (such as driving a vehicle or playing a game against an opponent) As it navigates its problem space, the program is provided feedback that's analogous to rewards, which it tries to maximise. [3]

Other approaches or processes have since developed that don't fit neatly into this three-fold categorisation, and sometimes more than one is used by the same machine learning system. For example topic modeling, dimensionality reduction or meta learning. [8] As of 2020, deep learning has become the dominant approach for much ongoing work in the field of machine learning .

<source wikipedia >


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