Nex-G Skills

"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."

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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 tasks are classified into three categories.

  • Supervised learning
  • Semi-Supervised learning
  • Unsupervised learning

Supervised learning, the algorithm are used to build a mathematical model of a set of data that contains both the inputs and the desired outputs.

Semi-supervised learning algorithms develop mathematical models from incomplete set of data, where a portion of the sample inputs are missing the desired output.

Unsupervised learning, the algorithm builds a mathematical model of a set of data which contains only inputs and no desired outputs. Unsupervised learning algorithms are used to find structure in the data, like grouping or clustering of data points. Unsupervised learning can discover patterns in the data, and can group the inputs into categories

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.

About sixty-one percent of organizations most frequently pick machine learning and artificial intelligence as their company’s most significant data intuitive for next year. More than half of these companies indicating that they actively use machine learning and artificial intelligence stated they ran models in production.Total Available Market (TAM) for machine learning accelerator technologies could potentially reach about twenty six billion dollars by 2020.

The pace of AI job posting growth speed up over the past year alone. AI alone will create 2.3 million jobs globally by 2021. The top 10 most in-demand AI jobs right now Data scientist, Software engineer, Machine learning engineer, Software architect, Data analyst, Data warehouse engineer, Full stack developer, Research scientist, Front end developer, Product manager.

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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