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 R Training

Machine learning is a branch in computer science that studies the design of algorithms that can learn. Typical machine learning tasks are concept learning, function learning or “predictive modeling”, clustering and finding predictive patterns. These tasks are learned through available data that were observed through experiences or instructions, for example. Machine learning hopes that including the experience into its tasks will eventually improve the learning. The ultimate goal is to improve the learning in such a way that it becomes automatic, so that humans like ourselves don't need to interfere any more.

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.

Machine Learning Training Objectives

  • Conceptual understanding of learning applications, formulation of learning tasks as computational problems, and methods that are designed to solve these problems
  • Understanding the commonalities and differences between different learning tasks and different approaches to learning
  • Understanding the trade-offs in developing solutions
  • Thorough understanding of rigorously evaluating the performance of learning algorithms
  • Ability to manipulate, extend, and apply machine learning methods and algorithms in the context of real-world problems

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