MACHINE LEARNING
Machine learning is a method of data analysis that automates analytical model building. It is a division of artificial intelligence based on the notion that machines should be able to learn and adapt through experience. Machine learning enables the computers to get into a mode of self-learning without being explicitly programmed. When exposed to new data, these computer programs are enabled to learn, raise, change, and progress by themselves. While the concept of machine learning has been around for a long time, the ability to apply complex mathematical calculations to big data automatically—iteratively, and speedily has been gaining momentum over the last several years. So, explore this article and know more about machine learning and its importance.
MACHINE LEARNING
To understand the uses of machine learning, consider some of the examples where machine learning is applied: the self-driving Google car, cyber fraud detection, online recommendation engines such as friend suggestions on Facebook and much more are all examples of applied machine learning.
Machines can aid in filtering useful pieces of information that help in major advancements, and we are already seeing how this technology is being implemented in a varied variety of industries. Machine learning has also improved the way data extraction, and interpretation is done by involving automatic sets of generic methods that have replaced traditional statistical techniques.
THE USES OF MACHINE LEARNING
To understand the idea of machine learning better, let’s consider some more examples: web search results, real-time ads on website pages and mobile devices, email spam filtering, network intrusion detection, and pattern and image recognition.
All these are by-products of applying machine learning to analyse huge volumes of data.
Traditionally, data analysis was always been characterized by trial and error method, an approach that becomes impossible when data sets are huge and heterogeneous. Machine learning comes as the solution to all this anarchy by proposing shrewd alternatives to analyse huge volumes of data. By developing fast and efficient algorithms and data-driven models for real-time processing of data, machine learning is able to produce correct results and analysis.
TERMS AND TYPES
Machine learning is one of the most important technology trends at present. It underlies so many things we use today without even thinking about them. Speech recognition, Amazon, and Netflix recommendations, fraud detection, and financial trading are just a few well known examples of machine learning commonly used in today’s data-driven world.
POPULAR MACHINE LEARNING METHODS
Two popular methods of machine learning are supervised learning and unsupervised learning. It is estimated that about 70 percent of machine learning is supervised learning, while unsupervised learning ranges from 10 – 20 percent. Other methods are less-often used are semi-supervised and reinforcement learning.
SUPERVISED LEARNING
Supervised machine learning is more commonly used between the two. It includes algorithms such as linear and logistic regression, multi-class classification, and support vector machines. Supervised learning requires that the algorithm’s possible outputs are already known and that the data used to train the algorithm is already labelled with correct answers. For instance, a classification algorithm will learn to identify animals after being trained on a dataset of images that are properly labelled with the species of the animal and some identifying characteristics.
UNSUPERVISED LEARNING
Unsupervised machine learning is more thoroughly aligned with true artificial intelligence — the idea that a computer can learn to identify complex processes and patterns without a human to provide guidance along the way. Although unsupervised learning is prohibitively complex for some simpler enterprise use cases. Some examples of unsupervised machine learning algorithms include “K” k-means clustering, principal, and independent component analysis, and association rules.
CONCLUSION
Choosing machine learning typically depends on factors related to the structure and volume of user’s data and the use case of the issue at hand. Predictive data models will help the users to make decisions across a variety of business challenges.