The SciPy module for science & math computing in python
The MatPlotLib module for making plots in python
scikit-learn for python data mining and machine learning
The Jupyter notebook for scientific computing
Theano is a Python library that integrates with NumPy to let you optimize multi-dimensional array math efficiently.
Google's Python Class is a great tutorial on Python.
The Beginner's Guide to Python
The Beginner's Guide to MatPlotLib
The Deep Learning list of software links
Weka data mining toolkit
The libsvm support vector machine tool kit
HTK hidden Markov model toolkit
Java NNS neural net toolkit
Matlab NNSYSID neural net toolkit
NNinExcel neural nettoolkit
Torch is a scientific computing framework with wide support for machine learning algorithms.
TensorFlow is Google's open source software library (intended for DNNs) for numerical computation using data flow graphs.
University of California Irvine Machine Learning Dataset Repository has many classic datasets.
The Movielens collaborative filtering dataset
The Jester collaborative filtering joke dataset
Datasets for the book The Elements of Statistical Learning
The Yale Face dataset - The database contains 5760 single light source images of 10 subjects each seen under 576 viewing conditions (9 poses x 64 illumination conditions).
The University of Iowa Instrument Recording repository - If you want to learn the difference between different musical instrument sounds, this is a useful set.
The Statlab collection of datasets - Has things like a pre-classified dataset containing 11,000 web pages from 11 different categories and data on salaries of Major League Baseball players.
Datasets used in the book Pattern Recognition and Machine Learning by Chris Bishop
The Elements of Statistical Learning
Reinforcement Learning: An Introduction by Richard S. Sutton (Author), Andrew G. Barto (Author)
Pattern Recognition and Machine Learning by Christopher Bishop
Adaptation in Natural and Artificial Systems by John Holland
Data Mining: Practical Machine Learning Tools and Techniques (Second Edition) - Ian H. Witten, Eibe Frank
Neural Networks for Pattern Recognition by Christopher M. Bishop
Neural Networks: A Comprehensive Foundation (2nd Edition) by Simon Haykin
Support Vector Machines and other kernel-based learning methods by John Shawe-Taylor Nello Cristianin
Causality, by Judea Pearl
American Association of Artificial Intelligence (AAAI) page on Machine Learning
The Deep Learning website
Machine Learning Journal
Journal of Machine Learning Research
Association of Artificial Intelligence Confrence (AAAI)
Neural Information Processing Systems (NIPS)
Machine Learning for Signal Processing (MLSP)
International Conference on Machine Learning and its Applications (ICMLA)
UC Irvine's Center for Machine Learning
Carnegie Mellon University's Machine Learning Department
Northwestern University's Qualitative Reasoning Group
The Reinforcement Learning Repository
Christopher Bishop (heads machine learning at Microsoft Research Cambridge)
Michal Jordan (he is a big guy in graphical models for learning)
Judea Pearl (known for his work on Bayesian reasoning)
Lawrence Rabiner (speech recognition and Markov models)
Robert Schapire (known for his work on Boosting)
Richard Sutton (he is a big guy in Reinforcement Learning)
Vladimir Vapnik (known for his work on Support Vector Machines)