Nex-G Innovations

"It was the most challenging but the best experience I had as I was novice in machine learning world. My Mentor was supportive and helpful all the time. With one word they are amazing. Nex-G was definitely the correct place to take machine learning related courses because we felt like a part of Nex-G family."


Machine Learning Training

Machine learning is a method of data analysis that automates analytical model building. Using algorithms that iteratively learn from data, machine learning allows computers to find hidden insights without being explicitly programmed where to look.

ml = representation + evaluation + optimization

It is the study of algorithms and statistical models that system uses to progressively improve their performance on a specific task for learning. This is achieved by building a mathematical model of sample data known as training data in order to make predictions or decisions on testing data without being explicitly programmed to perform the task.

Machine Learning - In Industry

Machine learning patents grew at a Compound Annual Growth Rate of thirty-four percent and had featured as the third faster-growing category of all patents granted. A forecast by International Data Corporation (IDC) indicates that spending on artificial intelligent (AI) and machine learning (ML) will grow from twelve billion dollars in 2017 to about fifty-eight billion dollars in 2021. A prediction also shows that the number of machine learning pilot and implementations will double in 2018 compared to 2017, and double again by 2020. Coupled with a lot of other fascinating insights are the latest series of machine learning market forecasts, project ions, and market estimates.

Why is Machine Learning important ?

Resurging interest in machine learning is due to the same factors that have made data mining and Bayesian analysis more popular than ever. Things like growing volumes and varieties of available data, computational processing that is cheaper and more powerful, and affordable data storage. All of these things mean it's possible to quickly and automatically produce models that can analyze bigger, more complex data and deliver faster, more accurate results – even on a very large scale. And by building precise models, an organization has a better chance of identifying profitable opportunities – or avoiding unknown risks.

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