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This repository contains my project from MIT Big data and Social Analytics certification completed in Fall 2016

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Atrificial Intelligence

AI is defined as ability of a machine to perform cognitive functions we associate with human minds, such as perceiving, reasoning, learning, interacting with the environment, problem solving, and even exercising creativity. Examples of technologies that enable AI to solve business problems are robotics and autonomous vehicles, computer vision, language, virtual agents, and machine learning.

simple definition of AI

Source Kdnuggets

Machine-Learning

Most recent advances in AI have been achieved by applying machine learning to very large data sets. Machine-learning algorithms detect patterns and learn how to make predictions and recommendations by processing data and experiences, rather than by receiving explicit programming instruction. The algorithms also adapt in response to new data and experiences to improve efficacy over time.

This repository is a knowledge and learning hub that contains all resources relating to machine learning

Ml-Approach Description Notebooks
Supervised Learning Machine Learning information on Supervised learning approaches
Un-Supervised Learning Show file differences that haven't been staged
Semi-Supervised Learning Show file differences that haven't been staged

Machine Learning Tools

General Purpose Machine Learning

  • scikit-learn- Machine learning in Python
  • cuML-RAPIDS Machine Learning Library.
  • Dask-Flexible library for parallel computing in Python.

Deep Learning Frameworks

  • TensorflowTensorFlow is an end-to-end open source platform for machine learning

  • PyTorch-Open source ML and DL framework

  • Chainer - A Powerful, Flexible, and Intuitive Framework for Neural Networks. Supports CUDA computation & requires a few lines of code of GPU

  • Regenerative Models


Data-Driven Use Cases

ML IN Production

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This repository contains my project from MIT Big data and Social Analytics certification completed in Fall 2016

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