"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 algorithms use computational methods to “learn” information directly from data without assuming a predetermined equation as a model. They can adaptively improve their performance as you increase the number of samples available for learning.
Machine learning algorithms are used in applications such as computational finance (credit scoring and algorithmic trading), image processing and computer vision (face recognition, object detection, object recognition), computational biology (tumor detection, drug discovery, and DNA sequencing), energy production (price and load forecasting), natural language processing, speech and image recognition, and advertising and recommendation systems.
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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