Machines at Work: Understanding the Ins and Outs of AI and Machine Learning
Machines at Work: Understanding the Ins and Outs of AI and Machine Learning
Read this article in order to learn more about artificial intelligence, machine learning, and deep learning and what each of these mean for businesses.
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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.
Expressing Machine Learning With a Programming Language
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.
The Working Principle of Machine Learning Algorithms
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. An understanding of the different components of the Machine Learning algorithm and their inter-relationship can make the Machine Learning task easier.
Machine Learning algorithms have a structured learning component that gives them the power to comprehend patterns in the input data that consequently lead to the output.
Input Data -> Pattern -> Machine Learning Algorithm -> Inference/Output
Let "Y" represent the future predictions and "X" represent the input samples. Then, we have the expression:
Where "Y" is also called the mapping function and "f" is called the target function. "f" is always unknown since it cannot be determined mathematically. Thus, Machine Learning is used to get an approximation of the target function, "f." The Machine Learning algorithm takes into consideration several assumptions regarding the target function and begins with its estimation with a hypothesis. To get the best estimate of the output, a number of iterations of the hypothesis are carried out. It is this hypothesis that enables the Machine Learning algorithm to get a better approximation of the target function in a short time span.
Artificial Intelligence vs. Machine Learning vs. Deep Learning
Your aspirations must never be clouded by vagueness. Artificial Intelligence, Machine Learning, and Deep Learning are terms often used interchangeably, which more or less add up to the already existing confusion associated with these terms. Let us grasp these concepts and get their connotations and nuances straight.
Artificial Intelligence is a concept much broader than Machine Learning. It is about imparting human-like cognitive intelligence to computers. Any machine that carries out a task in an intelligent manner with the use of algorithms, it is said to display artificial intelligence.
Machine Learning is a subset of AI. It is about the ability of machines to learn from a set of data. This learning by information processing enhances the algorithm, thereby providing better estimates and future predictions.
Deep Learning dives deeper into Machine Learning and can be thought of as a subset of Machine Learning. Neural networks allow computers to mimic the human brain. Just like our brain has the innate capability to recognize patterns that allow categorizing and classifying information, neural networks achieve the same for computers. Deep Learning is also sometimes called deep neural networks due to the numerous layers of nested hierarchy of decision trees as millions of data points.
Making Your Machine Learning Artificial Intelligence Certification Count
A recent report by Google concluded that since the last 18 months, the interest in Machine Learning has doubled. In this age of innovation and disruption, technology landscape changes rapidly. Yesterday’s buzzword may become a cliché today, and plunge into the chasm of oblivion tomorrow; who knows? There is a constraint of time in learning new technologies. One has to be up on their toes to remain updated and upgraded.
Machines have been driving our existence since the first industrial revolution to the current trend of industry 4.0. It is, thus, somewhat imperative to be an integral part of this revolution by making you well acquainted with a formidable technology platform like Advanced Machine Learning, AI, and Deep Learning. Once you are through with its ins and outs, success will be on the horizon to embrace you!
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