ML and Data Science
Online Courses
AI and Machine Learning
Machine Learning - Stanford University via Coursera - completed
Machine learning is the science of getting computers to act without being explicitly programmed. This course provides a broad introduction to machine learning, datamining, and statistical pattern recognition.
Introduction to Artificial Intelligence - Stanford University via Udacity - in progress
This class introduces students to the basics of Artificial Intelligence, which includes machine learning, probabilistic reasoning, robotics, and natural language processing.
Overview of AI
Statistics, Uncertainty, and Bayes networks
Machine Learning
Logic and Planning
Markov Decision Processes and Reinforcement Learning
Hidden Markov Models and Filters
Adversarial and Advanced Planning
Image Processing and Computer Vision
Robotics and robot motion planning
Natural Language Processing and Information Retrieval
Artificial Intelligence for Robotics - Stanford University via Udacity
This class introduces students to the basics of Artificial Intelligence, which includes machine learning, probabilistic reasoning, robotics, and natural language processing.
Data Science
Source for picture: InsideBigData
Data Science Specialization - Johns Hopkins University via Coursera
R Programming - done
Getting and Cleaning Data - done
Exploratory Data Analysis - done
Reproducible Research - done
Statistical Inference - in progress
Regression Models
Practical Machine Learning
Developing Data Products.
Course Wiki with additional tutorials and documentation
Course GitHUB with all the R code and slides (!)
Swirl: swirl teaches you R programming and data science interactively, at your own pace, and right in the R console!
Advanced R by Hadley Wickham ** to be checked **
Documentation and Wiki Sites
Foundations of Data Science by Microsoft Research - John Hopcroft and Ravindran Kannan
You can download the book here.
List of chapters:
Introduction
High-Dimensional Space
Best-Fit Subspaces and Singular Value Decomposition (SVD)
Random Graphs
Random Walks and Markov Chains
Learning and VC-dimension
Algorithms for Massive Data Problems
Clustering
Topic Models, Hidden Markov Process, Graphical Models, and Belief
Other Topics
Rankings
Hare System for Voting
Compressed Sensing and Sparse Vectors
Applications
Gradient
Linear Programming
Integer Optimization
Semi-Definite Programming
An Introduction to Statistical Learning with Applications in R (4th Edition) - G. Casella, S. Fienberg, I. Olkin
This book provides an introduction to statistical learning methods.
It is aimed for upper level undergraduate students, masters students and Ph.D. students in the non-mathematical sciences.
The book also contains a number of R labs with detailed explanations on how to implement the various methods in real life settings, and should be a valuable resource for a practicing data scientist.
You can download the book here
For more informations and resources: http://www-bcf.usc.edu/~gareth/ISL/
Tidy Data - Hadley Wickham
A huge amount of effort is spent cleaning data to get it ready for analysis [...]
This paper tackles a small, but important, component of data cleaning: data tidying. [...]
You can download the research paper here
The Field Guide to Data Science - Booz, Allen, Hamilton
This field guide came from the passion our team feels for its work.
It is not a textbook nor is it a superficial treatment. Senior leaders will walk away with a deeper understanding of the concepts at the heart of Data Science.
Practitioners will add to their toolbox. We hope everyone will enjoy the journey.
You can download the book here
Mining of Massive Datasets (2nd edition) - Jure Leskovec, Anand Rajaraman, Jeff Ullman - NEW
Big-data is transforming the world. Here you will learn data mining and machine learning techniques to process large datasets and extract valuable knowledge from them.
You can download the book here
http://www.boozallen.com/media/file/The-Field-Guide-to-Data-Science.pdf
Unsupervised Feature Learning and Deep Learning - Wiki (Stanford University)
This tutorial will teach you the main ideas of Unsupervised Feature Learning and Deep Learning. This tutorial assumes a basic knowledge of machine learning (specifically, familiarity with the ideas of supervised learning, logistic regression, gradient descent).
Software
Project Name: Pylearn2
Language: Python
Project URL: http://deeplearning.net/software/pylearn2/
Description:
Pylearn2 is a machine learning library. Most of its functionality is built on top of Theano. This means you can write Pylearn2 plugins (new models, algorithms, etc) using mathematical expressions, and Theano will optimize and stabilize those expressions for you, and compile them to a backend of your choice (CPU or GPU).
Project Name: DeepLearnToolbox
Language: Matlab/Octave
Project URL: https://github.com/rasmusbergpalm/DeepLearnToolbox/
Description:
Matlab/Octave toolbox for deep learning.
Includes Deep Belief Nets, Stacked Autoencoders, Convolutional Neural Nets, Convolutional Autoencoders and vanilla Neural Nets.
Each method has examples to get you started.
Project Name: DeepDive
Language: PostgreSQL, Scala, Python2
Project URL: http://deepdive.stanford.edu/
Description:
DeepDive is a new type of system that enables developers to analyze data on a deeper level than ever before. DeepDive is a trained system: it uses machine learning techniques to leverage on domain-specific knowledge and incorporates user feedback to improve the quality of its analysis.
Project Name: MADlib
Language: C++, Python
Project URL: http://madlib.net/ + https://github.com/madlib/madlib/
Description:
MADlib is an open-source library for scalable in-database analytics.
It provides data-parallel implementations of mathematical, statistical and machine learning methods for structured and unstructured data.
Apache UIMA.
Unstructured Information Management (UIMA) is a standard for performing analysis on textual content.
OpenCog ...
OAQA ...
Web Sites
A.I. and Machine Learning
Deep Learning is a new area of Machine Learning research, which has been introduced with the objective of moving Machine Learning closer to one of its original goals: Artificial Intelligence. This website is intended to host a variety of resources and pointers to information about Deep Learning.
URL: http://startup.ml/ + http://startup.ml/blog/
Machine Learning Accelerator.
Data Science
Data Science Central is the industry's online resource for big data practitioners.
From Analytics to Data Integration to Visualization, Data Science Central provides a community experience that includes a robust editorial platform, social interaction, forum-based technical support, the latest in technology, tools and trends and industry job opportunities.
OpenCPU
OpenCPU is a system for embedded scientific computing and reproducible research.
The OpenCPU server provides a reliable and interoperable HTTP API for data analysis based on R. You can either use the public servers or host your own.
Demo site: https://public.opencpu.org/ocpu/test/
Data Visualization
