Supervised Learning And Unsupervised Learning
Published: 2021-05-12 09:21:02


                    SUPERVISED LEARNING

Supervised learning, as the name indicates, has the presence of a supervisor as a teacher. Basically supervised learning is when we teach or train the machine using data that is well labeled. Which 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 the supervised learning algorithm analyses the training data(set of training examples) and produces a correct outcome from labeled data. 

For instance, suppose you are given a basket filled with different kinds of fruits. Now the first step is to train the machine with all different fruits one by one like this:
 
***If the shape of the object is rounded and has a depression at the top, is red in color, then it will be labeled as –Apple.
***If the shape of the object is a long curving cylinder having Green-Yellow color, then it will be labeled as –Banana. 

Supervised learning deals with or learns with “labeled” data. This implies that some data is already tagged with the correct answer.

Types:-

1)Regression
2)Logistic Regression
3)Classification
4)Naive Bayes Classifiers
5)K-NN (k nearest neighbors)
6)Decision Trees
7)Support Vector Machine
 
Advantages:-

*Supervised learning allows collecting data and produces data output from 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.
 
                     UNSUPERVISED LEARNING

Unsupervised learning is the training of a machine using information that is neither classified nor labeled and allowing the algorithm to act on that information without guidance. Here the task of the machine is to group unsorted information according to similarities, patterns, and differences without any prior training of data. 

Unlike supervised learning, no teacher is provided that means no training will be given to the machine. Therefore the machine is restricted to find the hidden structure in unlabeled data by itself.

Unsupervised learning is classified into two categories of algorithms: 
 CLUSTERING : A clustering problem is where you want to discover the inherent groupings in the data, such as grouping customers by purchasing behavior.
ASSOCIATION : An association rule learning problem is where you want to discover rules that describe large portions of your data, such as people that buy X also tend to buy Y.

Unsupervised Learning Algorithm :
Unsupervised Learning Algorithms allow users to perform more complex processing tasks compared to supervised learning. Although, unsupervised learning can be more unpredictable compared with other natural learning methods. Unsupervised learning algorithms include clustering, anomaly detection, neural networks, etc.

Applications of unsupervised machine Learning :


Some applications of unsupervised machine learning techniques are:

**Clustering automatically split the dataset into groups base on their similarities.
**Anomaly detection can discover unusual data points in your dataset. It is useful for finding fraudulent transactions.
**Association mining identifies sets of items which often occur together in your dataset.
**Latent variable models are widely used for data preprocessing. Like reducing the number of features in a dataset or decomposing the dataset into multiple components.

Disadvantages of Unsupervised Learning:

**You cannot get precise information regarding data sorting, and the output as data used in unsupervised learning is labeled and not known.
**Less accuracy of the results is because the input data is not known and not labeled by people in advance. This means that the machine requires to do this itself.
**The spectral classes do not always correspond to informational classes.
**The user needs to spend time interpreting and label the classes which follow that classification.
**Spectral properties of classes can also change over time so you can't have the same class information while moving from one image to another.