Supervised learning is the task of inferring a function from labeled training data.
The training data consist of a set of training examples.
In supervised learning, each example is a pair consisting of an input object and the desired output value. A supervised learning algorithm analyzes the training data and produces an inferred function.
Supervised Learning: two process
Supervised learning is further divided into
Supervised Learning algorithms
K Nearest Neighbour
Linear SVC (Support Vector Classifier)
It allows you to be very specific about the definition of the labels.
You can determine the number of classes you want to have.
The input data is very well known and is labeled.
The results produced by the supervised method are more accurate.
Supervised learning can be a complex method.
Supervised learning needed a lot of computation time for training.
Unsupervised learning is where you only have input data (X) and no corresponding output variables.
The goal is to model the underlying structure or distribution in the data in order to learn more about the data.
These are called unsupervised learning because unlike supervised learning above there are no correct answers and there is no teacher.
Algorithms are left to their own devices to discover and present the interesting structure in the data.
Unsupervised learning is further divided into
Unsupervised Learning algorithms
The list of unsupervised learning algorithms are:
K-NN (k nearest neighbors)
Less complexity in comparison with supervised learning.
It is often easier to get unlabeled data.
Takes place in real-time such that all the input data to be analyzed and labeled in the presence of learners.
You cannot get very specific about the definition of the data sorting and the output.
Less accuracy of the results.
The results of the analysis cannot be ascertained.