Some Basic Machine Learning Models Everyone Need To Know

Machine learning skills are among the most sought after skills around the world at this moment. Businesses planning to integrate artificial intelligence based solutions in their systems are in dire need of trained machine learning engineers. If you feel that there may be a career in machine learning and AI for you, then you should start procuring knowledge about the fields. You can always enroll for an AI and machine learning course, but before you do that you should familiarize yourself with certain key ideas and concepts. You can read about how to learn AI and machine learning on your own, to make a start. Here you will find a dose of information about various machine learning models, information that you will need for sure. 

What is machine learning?

I am assuming you already know this, but I will just leave a short explanation. You might as well skip it. Machine learning is the process of training machines to perform certain tasks while improving itself through exposure to data. Machine learning engineers use different machine learning models for different tasks. Suppose you need to build a machine learning algorithm that can find the people who had the body temperature of more than 99 degree Fahrenheit from a large list of people along with their corresponding body temperature. It can be done with a simple function on any spreadsheet app. But if you try to categorise further factoring in people who have travelled from a different city or their hometown, you would need a machine learning algorithm. 

Machine learning models

There are three primary machine learning models – supervised learning, unsupervised learning, and reinforced learning. We will talk at length about the first two. We will look at various subcategories of these models and explore their areas of application.

Supervised learning

In this mode of machine learning the computer reaches the output with the help of a series of input output examples. Let us try to understand how it works.

Let us say you are developing a model that matches people with their shoe sizes according to their age and height. The training data would contain the age and height of people and the true size of their shoes. Supervised learning tries to figure out the shoe size of a person according to his or her age and height depending upon the labeled data it had received. Well, this is a very simplistic example, but it is what it is.

Subcategories of supervised learning

The two main types of supervised learning models are regression models and classification models. Regression tries to find a target value based on independent predictors. Classification reaches a discrete output.

Types of regression

One of the four main techniques of regression models is Linear regression. It finds the line that fits the data. Linear regression has two different kinds.

  • Multiple Linear regression: Finds a plane for the best fit.
  • Polynomial linear regression: Finds a curve for the best fit.

Next up is the decision tree which is just what it sounds like. One node leading to three others and so on. 

A number of decision trees put together make random forests. 

Then of course, there are neural networks, which we know, emulates the working principle of the human brain. 

Classification

This type of machine learning model tries to draw a conclusion based on the observed data.

The techniques used for classification are Logistic regression. Support vector machine, Naive Bayes, Decision trees, random forests, and neural networks.

Unsupervised learning

In this mode of machine learning the machine locates patterns in input data without any reference to labeled outcomes. There are two primary techniques of unsupervised learning.

Clustering

This method helps in grouping the data points. It is used for

  • Customer segmentation
  • Fraud detection 
  • Document classification

The different models used for clustering are K-means clustering, Hierarchical clustering, Mean shift clustering, and density based clustering.

Dimensionality reduction

This is used to reduce the dimensions of your feature set.

The two primary kinds of dimension reduction are feature elimination and feature extraction.

Then there is Principal Component Analysis or PAC. This is a sort of dimensionality reduction that takes a large data set and converts it into a smaller data set which contains the principal components of the larger set.

So, these were some of the machine learning models that you need to familiarize yourself with. You need a deep knowledge of the statistical computing and programming languages to code your way towards building and implementing such models to solve business related problems.