Machine Learning A whole new world of Possibilities

Introduction

Machine learning has been one of the few buzzwords of the 21st century that are here to stay. Surprisingly enough, machine learning has been present in theory longer than we could imagine.

But what is machine learning? It is the ability of a computer to learn from data and improve on its abilities to predict, analyze or create more data without being explicitly programmed.

In order to clearly understand ML, there is an inherent need to understand what Artificial Intelligence is. Though often used interchangeably in layman terms, they differ from each other on many levels.

Artificial Intelligence

Artificial Intelligence (AI) is an umbrella term for technologies that enable machines to mimic human intelligence, and consequently transform industries. These technologies include, but are not limited to, computer vision, language processing and machine learning.

The distinction between AI and ML

Almost the entire the economic value of AI is generated via Machine Learning algorithms which take an input

A subset of AI, Machine learning employs algorithms that learn from data to make predictions or decisions, and whose performance improves when exposed to more data over time. Machine Learning’s primary aim is to allow the computers to learn automatically without human intervention or assistance and adjust actions accordingly.

Why Machine Learning?

Increased influx of data around the world since 2005 has led to a surge in the use and application of ML algorithms. These include regression, classification models which will be understood in the subsequent parts of the article.

Types of Machine Learning

Supervised Learning

In supervised or predictive learning, the goal is to learn a mapping from inputs x to outputs y, given a labeled set of input-output pairs:

Where D is the training set and N is the number of examples. each training input xi is a D-dimensional vector of numbers and yi is categorical or nominal variable from some finite set, yi? {1,..., C}

Based on Yi’s nature, the problem can be defined into the following types:

1. When yi is categorical, the problem is known as classification or pattern recognition
2. When yi is real-valued, the problem is known as regression

Unsupervised Learning

In unsupervised or descriptive learning only inputs are given: The goal is to find patterns that can be further employed to create new models. This is a much less defined problem since we are not aware of the patterns to find before-hand, and the scope for error is not defined.

Reinforcement Learning

This works on the principle of reward and punishment and is especially useful while learning how to behave or act. Though less commonly used, it is still a very useful technique.

Uses of Machine Learning

Machine learning is currently being employed for the following uses:

• Image Recognition
• Spam and Malware detection
• Predictions based on big data
• Voice recognition
• Social Media Analysis
• Video Surveillance
• Autonomous vehicles

...and counting!

Future of Machine Learning

While the future looks very bright for machine learning algorithms, it is, in fact, one of its subsets: Deep Learning that has taken the front seat. With multiple uses of deep learning coming about with applications via Artificial Neural Networks, it is evident that ML will significantly improve human lives and will help us stride further into new discoveries and inventions.

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Machine Learning Programmer, Machine Learning Specialist