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".

deep-learning

Repo for the Deep Learning Nanodegree Foundations program.

deep-learning-1

Deep Learning Reading List of some of the materials i found on the web for Deep Learning beginners.

deeplearningtutorials

Deep Learning Tutorial notes and code. See the wiki for more info.

deepmind-atari-deep-q-learner

The original code from the DeepMind article + my tweaks

dqn-tensorflow

Tensorflow implementation of Human-Level Control through Deep Reinforcement Learning

interview

Everything you need to kick ass on your coding interview

leetcode_python

This is a leetcode repository

math.156

lab and project code from math 156 Machine Learning

self-driving-car-nd

Udacity's Self-Driving Car Nanodegree project files and notes.

system-design-primer

Learn how to design large scale systems. Prep for the system design interview.

the-book

:green_book: THE Book on Full-Stack Web Application Development covering User Experience (UX) Design/Tests, HTML5, Responsive + Functional CSS, Functional JavaScript, Mobile/Offline/Security First, Progressive Enhancement, Node.js, Hapi.js, Redux, React (Native), Elm, Elixir+Phoenix, Continuous Integration/Deployment, Testing (UX/TDD/BDD), Performance-Driven-Development and much more!

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