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 part of AI is significant in light of the fact that as models are presented to new information, they can autonomously adjust. They have gained from past calculations to deliver solid, repeatable choices and results. It’s a science that is not new – but rather one that has increased crisp force.

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

ML for the business

Investing in Machine Learning will be like investing in mobile a decade ago – it can transform your business.

In the era of technology revolution, Machine Learning is strengthening its roots deep inside the industry. Machine Learning is the next frontier in data analysis. As organizations have access to more data, machine learning enables them to draw insights from the data at scale, at a level of granularity that ranges from single user interaction to worldwide trends and their impact on the planet. The use of those insights can also range from customizing an individual user’s experience at the pixel level to creating new products and business opportunities that don’t currently exist.

Putting it altogether

In parallel with raw computational power, complex new algorithms have been developed to enable data scientists to run models using all the accessible information. Previously models had to be generalized to simplify the analytical process, but machine understanding can now ingest 100% of the data generated by every asset or person. The result is a far higher degree of accuracy that would be achieved with human analysis.