Machines at Work: Understanding the Ins and Outs of AI and Machine Learning
Pattern recognition is a virtue that can reap tremendous benefits for businesses if leveraged properly. It can make business forecasting efficient and decision-making effective. Machine Learning (ML) studies these patterns and encodes the human decision-making process into algorithms. These algorithms can then be applied to several instances for arriving at meaningful conclusions. In this article, we will shed some light on understanding Machine Learning, its working principle, and how it is different. We’ll also try to perceive the importance of Machine Learning, and which AI and Machine Learning course is best suitable in a particular case.
Understanding Machine Learning With an Example
Forrester Research predicts that by 2020, businesses adopting Machine Learning, AI, and Deep Learning, the Internet of Things (IoT), and Big Data will take away more than $1.2 trillion from their less-informed peers.
Data is the key to Machine Learning. Algorithms learn from a certain amount of data and then apply that learning to make informed decisions. Netflix has a good idea about which show you’d want to watch next, and Facebook can identify you and your friends in a photograph thanks to ML.
Machine Learning is all about automated tasks, and its application spans a wide range of industries. A data security firm can employ ML to track down malware, while a finance company can use it to enhance their profitability. As an example, let us consider a flashlight that has been programmed to turn on whenever the word “dark” appears in a phrase. The several phrases that we will be using become the input data for the Machine Learning algorithm of the flashlight.
To solve the business complexities and bring about the technological innovation with Machine Learning, programming languages and frameworks are consistently being introduced and updated. Some programming languages come and go, while some remain relevant by standing the test of time. Two of the most formidable programming languages in the Machine Learning and AI circle are Python and R. There are other languages like Java, C++, Julia, SAS, MATLAB, Scala, and many more. Our discussion is, however, limited to only Python and R.
Python is popular, simple, and versatile. It is a portable language used on all the major platforms like Viz, Linux, Windows, Mac, and UNIX. Python is used not only as a general purpose language for web development, but also as a specialized language in scientific computing, data mining, and analytics. If there is one programming skill that recruiters in ML and AI prefer the most, it is Python.
R is another programming language suitable for Machine Learning, and it has a close association with statisticians and mathematicians. Now, while ML itself is closely related to the concepts of statistics, R for Machine Learning can reap tremendous benefits. If you wish to unlock patterns in large blocks of data, R is the language of choice, which was designed by statisticians and scientists to facilitate data analysis with ease.
Machine Learning algorithms estimate a predictive model that is generalized with a particular kind of data. It is, therefore, imperative to have a large number of examples that can be utilized by the Machine Learning algorithm to understand a system’s behavior. Now, when the Machine Learning algorithm is presented with new types of data, the system will be able to generate similar kind of predictions.
