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artificial-intelligence-and-machine-learning

Algorithm implementations and homework solutions for the Stanford's online courses

awesome-datascience

:memo: An awesome Data Science repository to learn and apply for real world problems.

awesome-deep-learning

A curated list of awesome Deep Learning tutorials, projects and communities.

awesome-machine-learning

A curated list of awesome Machine Learning frameworks, libraries and software.

awesome-machine-learning-python

Machine and Deep Learning in Python

caffe

Caffe: a fast open framework for deep learning.

caret

caret (Classification And Regression Training) R package that contains misc functions for training and plotting classification and regression models

cs273a-introduction-to-machine-learning

Introduction to machine learning and data mining How can a machine learn from experience, to become better at a given task? How can we automatically extract knowledge or make sense of massive quantities of data? These are the fundamental questions of machine learning. Machine learning and data mining algorithms use techniques from statistics, optimization, and computer science to create automated systems which can sift through large volumes of data at high speed to make predictions or decisions without human intervention. Machine learning as a field is now incredibly pervasive, with applications from the web (search, advertisements, and suggestions) to national security, from analyzing biochemical interactions to traffic and emissions to astrophysics. Perhaps most famously, the $1M Netflix prize stirred up interest in learning algorithms in professionals, students, and hobbyists alike. This class will familiarize you with a broad cross-section of models and algorithms for machine learning, and prepare you for research or industry application of machine learning techniques. Background We will assume basic familiarity with the concepts of probability and linear algebra. Some programming will be required; we will primarily use Matlab, but no prior experience with Matlab will be assumed. (Most or all code should be Octave compatible, so you may use Octave if you prefer.) Textbook and Reading There is no required textbook for the class. However, useful books on the subject for supplementary reading include Murphy's "Machine Learning: A Probabilistic Perspective", Duda, Hart & Stork, "Pattern Classification", and Hastie, Tibshirani, and Friedman, "The Elements of Statistical Learning".

datascienceresources

Open Source Data Science Resources.

deeppy

Deep learning in Python

machine-learning-abb

machine-learning-coursera

Programming assignments from Coursera's Machine Learning course taught by Andrew Ng.

mlnotes

Very concise notes on machine learning and statistics.

mxnet.jl

MXNet Julia Package - flexible and efficient deep learning in Julia

parallel_ml_tutorial

Tutorial on scikit-learn and IPython for parallel machine learning

recurrent-neural-networks

Recurrent Neural Network for modeling sequential data implemented using Python and Theano.

scikit-learn

scikit-learn: machine learning in Python

stanford-machine-learning-course

machine learning course programming exercise

udemy---machine-learning

Notebooks for Course

unsupervised-learning