Understanding the Basics of Machine Learning Algorithms

Posted By : Akash Poria | 30-Apr-2020

 

Artificial intelligence (AI) and its underlying technologies, namely machine learning and deep learning are poised to transform industrial operations to generate significant value. With algorithmic advancements, more and more businesses are able to harness artificial intelligence development services to build dynamic machine learning solutions. However, it is imperative to understand the core principles of machine learning in order to build agile AI solutions that fulfill business needs efficiently. 


 

The Origins of Machine Learning

 

Machine Learning is defined as the field of study that powers computers to learn tasks without being explicitly programmed. It was given by Arthur Samuel in 1959. It explains the mechanism of machine learning but it is a 60-year-old definition to explain the concept in these modern times. As we moved closer to the 21st century, Tom Mitchell gave a new definition in 1998 and it is considered as a well-posed definition for machine learning and accepted by the whole community. He says-

 

A computer program is able to learn from some experience, say  E1 with respect to some task T1, and some performance measure P1. Machine learning is demonstrated if the computer’s performance on T1, as measured by P1, improves with experience E1.


 

Types of Machine Learning Algorithms 

 

Machine Learning Algorithms are divided into 4 categories which are further divided into a number of sub-categories. We will be going through two of the basic ML Algorithm categories and a few of their subcategories. 

 

  1. Supervised Learning

 

In a given data set, we already know what the correct output should look like, having the idea that there is a relationship between input and output is how we define supervised learning. It is a spoon-feed version of machine learning algorithms where you select what kind of information samples to “feed” the algorithm and what kind of results are desired. Supervised learning algorithms are further categorized into "Regression" and "Classification" algorithms.

 

a) Regression: In regression problems, we make efforts to predict results within the continuous data stream, suggesting that we map input variables to some continuous function. Example- predicting the price of a house according to its size.

 

  b) Classification: In classification problems, we are trying to predict results in a discrete output. In other words, we are trying to map input data into discrete categories. Example- Breast cancer prediction, yes or no problems, etc.

 

  1. Unsupervised Learning

 

Unsupervised learning enables us to approach problems with an ambiguous idea about what the final results should appear to be. It can deduce structure from data where we don't necessarily know the effect of variables. It can deduce this structure by clustering the data based on relationships among the variables in the data. Under unsupervised learning, we cannot deduce any feedback based on the results from the prediction. In other words, an unsupervised machine learning algorithm parses through data to understand its context and describe the same in human-understandable languages. It is basically used for exploring the structure of the information, extracting valuable insights, detecting patterns, and implementing this into its operation to increase efficiency. Unsupervised learning algorithms work on the basis of "Clustering".

 

 

  1. Clustering: It is an exploration of data used to segment it into clusters based on their internal patterns without prior knowledge of group credentials. We can define clusters by assessing the similarity withing individual data objects and also mapping the dissimilarity from the rest of the data. For instance, analysis of vast social networks, astronomical data analysis, etc.
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