Artificial Intelligence.

Hello,friends my name is darshak and it is my first blog. i am a computer science engineering student and enthusiastic to learn AI and ML. so without delay lets start,

What is Artificial Intelligence(AI) :-

AI is a combination of two words : Artificial  +  Intelligence.

Artificial :- Terms show case something which are made by human beings or non natural things.

Intelligence:- It means ability to understand and think.

Definition:- It is the study of how to train the computers so that computers can do things which is done by humans.

What is Machine Learning(ML) :-

Definition:- Machine learning is field of study that gives computers the ability to learn without being explicitly programmed, this definition is given by Arthur Samuel.

To know more about Arthur Samuel :-  https://en.wikipedia.org/wiki/Arthur_Samuel  

 ML is classified into three types:-

  1) Supervised Learning

  2) Unsupervised Learning

  3) Reinforcement Learning

     1) Supervised Learning:-

                                             Supervised Learning is a learning in which we teach or train the machine  using data which is well labeled that means data is already tagged with correct answer afterwards, machine is provided with new set of data so that supervised learning algorithm analyze the training data and produce a true output from labeled data.

There are two types of algorithms,

          A) Classification:-

                                         A classification problem is when the output variable is a category such as red or pink, cat or dog, male or female, mail is spam or not spam etc.

classification algorithms like:- Random forest , Support Vector Machine, k-nearest neighbor etc.

          B) Regression:-

                                        Regression is when output variable is real value such as, predict price of house,predict weight of a human from age etc.
Regression algorithms like:- Linear Regression , Logistic Regression etc.

     2) Unsupervised Learning:-

                                                   It is when you have only input data and its doesn't have a corresponding output variables.
The goal for Unsupervised learning is to model the underlying structure or distribution in data in order to learn more about the data.

There are two types of algorithms,

          A) Clustering:-
                                    Grouping in the data. such as, grouping customers by purchasing behavior, set of news articles found on web, group them into set of articles about the same story.
Clustering algorithms like:- K-means algorithms

          B) Association:-
                                      An association rule learning problem is where you want to discover rules that describe large portions of your data, like people buy x thing also tend to buy y thing.
Association algorithms like:- Apriori algorithms

     3) Reinforcement Learning:-

                                                     There is training data with no labeled but your reinforcement agent still has to decide how to act to perform its task, here agent learn from it's past experience by trial and error.
Reinforcement learning algorithms like:- Q-learning, Sarsa etc.


Thank you for reading......