There are three kinds of Machine Learning Algorithms.

a. Supervised Learning

b. Unsupervised Learning

c. Reinforcement Learning

a. Supervised Learning

A majority of practical machine learning uses supervised learning.

In supervised learning, the system tries to learn from the previous examples that are given. (On the other hand, in unsupervised learning, the system attempts to find the patterns directly from the example given.)

Speaking mathematically, supervised learning is where you have both input variables (x) and output variables(Y) and can use an algorithm to derive the mapping function from the input to the output. The mapping function is expressed as Y = f(X).

Example

Supervised learning problems can be further divided into two parts, namely classification, and regression.

Classification:

A classification problem is when the output variable is a category or a group, such as “black” or “white” or “spam” and “no spam”. This is a type of problem where we predict the categorical response value where the data can be separated into specific “classes” (ex: we predict one of the values in a set of values).

Some examples are :

• this mail is spam or not?

• will it rain today or not?

• is this picture a cat or not?

Basically ‘Yes/No’ type questions called binary classification.

Other examples are :

• this mail is spam or important or promotion?

• is this picture a cat or a dog or a tiger?

This type is called multi-class classification.

Regression:

A regression problem is when the output variable is a real value, such as “Rupees” or “height.” This is a type of problem where we need to predict the continuous-response value (ex : above we predict number which can vary from -infinity to +infinity) Some examples are • what is the price of house in a specific city? • what is the value of the stock? • how many total runs can be on board in a cricket game? etc… there are tons of things we can predict if we wish.

b. Unsupervised Learning

In unsupervised learning, the algorithms are left to themselves to discover interesting structures in the data. Mathematically, unsupervised learning is when you only have input data (X) and no corresponding output variables.

This is called unsupervised learning because unlike supervised learning above, there are no given correct answers and the machine itself finds the answers.

Unsupervised learning problems can be further divided into association and clustering problems.

• 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”.
• Clustering: A clustering problem is where you want to discover the inherent groupings in the data, such as grouping customers by purchasing behaviour.

c. Reinforcement Learning

A computer program will interact with a dynamic environment in which it must perform a particular goal (such as playing a game with an opponent or driving a car). The program is provided feedback in terms of rewards and punishments as it navigates its problem space. Using this algorithm, the machine is trained to make specific decisions. It works this way: the machine is exposed to an environment where it continuously trains itself using trial and error method.

Example: