Important Algorithms in Machine Learning

The concept of Machine Learning is gaining popularity day by day. It is a part of the family of Artificial Intelligence. Also, it is a hot topic for thesis and research. Machine Learning has the ability to make predictions and suggestions based on the learning imposed to the system. It is able to process a large amount of data.

How to learn Machine Learning?

For learning the process of Machine Learning, you need to learn the underlying algorithms responsible for the implementation of the whole process of Machine Learning.

Machine Learning algorithms can be divided into the following three categories:

1. Supervised Learning
2. Unsupervised Learning
3. Reinforcement Learning

Supervised Learning

Supervised Learning algorithms are those in which predictions are made based on the known dataset which is also known as training dataset. Following types of algorithms are included in supervised learning:

• Decision Trees - It is a tree-like graphical model of decisions and their possible outcomes. It provides a structural and systematic way to solve a problem to reach a logical solution. In business point of view, it is the minimum number of yes/no questions to make a correct decision.
• Naive Bayes Classification - These are the probabilistic classifiers based on the application of Baye's Theorem. 
• Regression - It is a statistical concept to measure the relationship between different variables. The regression may be least square regression or logistic regression. 
• Support Vector Machines - It is a type of binary classification algorithm. This algorithm produces an (N-1) dimensional hyperplane to separate points in N-dimensional space.
• Ensemble Methods - These are the type of learning algorithms that create a set of classifiers and classifies new data sets according to these classifiers.

Unsupervised Learning

It is a type of Machine Learning algorithm in which inferences are drawn from datasets having input data with no labels. It consists of the following types of algorithms:

• Clustering Algorithms - Clustering means grouping a set of objects into clusters such that these objects have similar characteristics. There are different types of clustering algorithms like centroid-based algorithms, probabilistic, deep learning.
• Principal Component Analysis - It is a statistical procedure in which correlated variables are converted into uncorrelated variables. 
• Singular Value Decomposition - It is a part of linear algebra in which there is factorization of a real complete matrix.
• Independent Component Analysis - It is a statistical technique in which hidden factors of random variables, measurements, and signals are revealed.

Reinforcement Learning

It is a machine learning algorithm which gives machines and software agents the ability to determine the ideal behavior with respect to the surrounding in order to maximize the performance.
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