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."


Modeling And Optimization For Machine Learning

PyBrain is a modular Machine Learning Library for Python. Its goal is to offer flexible, easy-to-use yet still powerful algorithms for Machine Learning Tasks and a variety of predefined environments to test and compare your algorithms. PyBrain is short for Python-Based Reinforcement Learning, Artificial Intelligence and Neural Network Library. In fact, we came up with the name first and later reverse-engineered this quite descriptive "Backronym"..

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

  • Recognize classes of optimization problems in machine learning and related disciplines.
  • Learn concepts that demystify the “why” and “how” of ubiquitous topics such as regression, deep learning, and large-scale optimization, with a focus on convex and non-convex models.
  • Interface with software for computing optimal solutions to a given machine learning problem.
  • Understand the mathematical underpinnings of optimization methods via examples drawn from machine learning, computer vision, engineering, and data analysis.
  • Understand foundational optimization ideas including gradient descent, stochastic gradient methods, higher-order methods, and more advanced optimization algorithms.
  • Classify optimization problems by their tractability, difficulty, and compatibility with existing software.
  • Learn to cut through the hype to make more informed choices for their own applications.

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