The time is now for Machine Learning

Have you heard your peers discussing Machine Learning (ML) yet have only a vague idea of what that implies? It is safe to say that you are burnt out on gesturing your way through discussions with collaborators? Let’s change that!

The way toward learning automation starts with perceptions of information, for example, models, direct understanding, or guidance. so as to search for examples in information and settle on better choices later on dependent on the models that we give. The primary aim is to allow the computers to learn automatically without human intervention and adjust actions accordingly. Machine Learning focuses on the development of computer programs that can access data and use it to learn for themselves.

Why Machine Learning?

You may ask why there is such a great amount in the Statistical Analysis System (SAS) report about Machine Learning (ML) now. Why now, when artificial intelligence (AI), the parent technology to machine learning (ML), has been around for more than 50 years? The reason is that there is an extraordinary convergence of large volumes of Big Data, unprecedented computing power, and sophisticated self-learning algorithms taking place. The affordability, viability, and feasibility of these three technologies are the driving forces behind why machine learning (ML) is becoming more and more prevalent today.

Evolution of Machine Learning

One can intuitively surmise machine learning (ML) is the present hot commodity, creating a strong impact on businesses, academia, and government in recent years. Presently, there is information – all in one place – that documents growth across many indicators, including startups, venture capital, job openings, and academic programs.

Because of new computing technologies, machine learning today is not like machine learning of the past. It was born from pattern recognition and the theory that computers can learn without being programmed to perform specific tasks; researchers interested in AI/ML wanted to see if computers could learn from data. The iterative aspect of machine learning is important because as models are exposed to new data, they are able to independently adapt. They learn from previous computations to produce reliable, repeatable decisions and results. It’s a science that’s not new – but one that has gained fresh momentum.

Why now?

• World’s data doubling every 2 years.
• Massive computation is available at a low cost.
• Ease of use of ML and AI platform
• New development tools and paradigms
• Adoption of natural user interfaces
• Expectations for apps to be intelligent

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