Akshitha

Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being
explicitly programmed.
Machine learning focuses on the development of computer programs that can access data and use it learn for themselve.


Supervised learning as the name indicates the presence of a supervisor as a teacher. Basically supervised learning is a learning in which we teach or train the machine
using data which is well labeled that means some data is already tagged with the correct answer.
After that, the machine is provided with a new set of examples(data) so that supervised learning algorithm analyses the training data(set of training examples) and produces a
correct outcome from labeled data. 

Supervised learning classified into two categories of algorithms: 
 
Classification: A classification problem is when the output variable is a category, such as “Red” or “blue” or “disease” and “no disease”.
Regression: A regression problem is when the output variable is a real value, such as “dollars” or “weight”.
Supervised learning deals with or learns with “labeled” data.Which implies that some data is already tagged with the correct answer.

Types:-

Regression
Logistic Regression
Classification
Naïve Bayes Classifiers
Decision Trees
Support Vector Machine
Advantages:-

Supervised learning allows collecting data and produce  data output from the previous experiences.
Helps to optimize performance criteria with the help of experience.
Supervised machine learning helps to solve various types of real-world computation problems.

Disadvantages:-

Classifying big data can be challenging.
Training for supervised learning needs a lot of computation time.So,it requires a lot of time