Machine Learning refers to the process of data analytic techniques that are able to guide machine and computers alike to learn, analyze and perform computational-informational data performance based on the their knowledge base.

Feeding algorithms improves and guides their performance depending on the sampling availability. There are majorly two ways of Machine learning, this includes Supervised learning and Unsupervised learning.

Supervised learning:

Supervised machine learning refers to the process that is built on educated predictions made by the machines in the presence of uncertainty. During supervised learning the algorithm takes an already available set of knowledge and input data, processes it immaculately and leads to an output set of results based on the available predictions.

It uses two learning techniques to develop predictive models. This includes a Classification and Regression techniques.

Classification Technique:

Classification technique in machine learning, allows the prediction of discrete responses by compiling them into categories, depending on the form of input data. It is able to do this by sorting out various genres of information and secreting responses methodically. Common examples includes

  • Neural networks
  • Logistical regression
  • Discriminant analysis etc
  • Support vector machines
  • Boosted/ bagged decision trees
  • Naïve Bayes
  • K-nearest

Regression Technique:

Regression technique allows the prediction of continuous responses in terms of real numbers and provides typical features like temperature changes, power fluctuations, algorithm trading and general forecasting.

Unsupervised Learning:

Unsupervised learning is able to analyze hidden patterns and intrinsic structures within a given data. It is able to draw specific datasets without any labeled responses based on the available information through input data.


It is able to perform using a technique called Clustering. Clustering is one of the most common examples of unsupervised learning techniques. It is able to assist with exploratory data analysis and find any gaps, hidden patterns or data grouping within a system.

Some of the application of unsupervised learning methodology includes the use of this technique in

  • market analysis
  • research
  • Object recognition
  • K-mediods
  • K-means
  • Self-organizing maps
  • Subtractive clustering
  • Fuzzy c-means
  • Hidden Markov Models
  • Hierarchical clustering

One great example of the use of this technique includes the process of using clustering in order optimize locations for the deployment of cell towers by cell phone companies. They use clustering data in order to figure out the amount of users who will be connected with the radar/cell tower and find the best location for deployment, in order to optimize and facilitate the users.

Machine Learning using MATLAB:

MATLAB has been designed after meticulous research to harness the power of machine learning and facilitate user’s multi dimensionally. It helps by providing the following set of features

  • Provide support and additional assistance with integrated workflows
  • Perform automatic coding for embedded sensor analytics
  • Provide assistance with integrated machine learning models through clusters, target models, real-time embedded hardware and cloud service
  • Assistance by extracting features using established automated methods
  • Compare logistical regression, support vector machines, deep learning and classification trees

With the advancement in terms of technology, you can now extract, compile and analyze multi-dimensional data fuss free!