Hackers_Introduction_to_Machine_Learning

A running collection of resources for people who want to get started in machine learning and data science

Open Courseware:

Mathematical Foundations:

Linear Algebra

Kahn Academy Linear Algebra Lectures:
https://www.khanacademy.org/math/linear-algebra MIT OCW 18.06 with Gilbert Strang (Linear Algebra):
https://www.youtube.com/watch?v=ZK3O402wf1c&list=PL49CF3715CB9EF31D

Probability and Statistics

Kahn Academy High School Statistics:
https://www.khanacademy.org/math/probability#table-of-contents MIT OCW 6.041 Probability and Statistics:
https://www.youtube.com/watch?v=j9WZyLZCBzs&list=PLQ3khvAsNhargDx0dG1cQXOrA2u3JsFKc https://www.youtube.com/watch?v=j9WZyLZCBzs&list=PLUl4u3cNGP60A3XMwZ5sep719_nh95qOe Harvard Statistics 110: Probability:
https://www.youtube.com/playlist?list=PL2SOU6wwxB0uwwH80KTQ6ht66KWxbzTIo

Deep Learning:

Andrew Ng Coursera (A good place to start):
https://www.coursera.org/learn/machine-learning

Originally taught as CS229 on Youtube by Andrw Ng:
https://www.youtube.com/watch?v=UzxYlbK2c7E&list=PLA89DCFA6ADACE599

Nando de Freitas Oxford Lectures on Deep Learning (Introductory)
https://www.youtube.com/watch?v=dV80NAlEins&list=PLE6Wd9FR--EfW8dtjAuPoTuPcqmOV53Fu

Neural Networks by Geoffrey Hinton (Formerly Coursera)

https://www.youtube.com/watch?v=cbeTc-Urqak&list=PLoRl3Ht4JOcdU872GhiYWf6jwrk_SNhz9

Probabalistic Graphical Models (Daphne Kohler)

https://www.youtube.com/watch?v=WPSQfOkb1M8&list=PL50E6E80E8525B59C

Natural Language Processing Stanford (Formerly Coursera)

https://www.youtube.com/watch?v=nfoudtpBV68&list=PL6397E4B26D00A269

Statistical Learning Theory

MIT Course 9.520 - Statistical Learning Theory and Applications

Fall 2015:
https://www.youtube.com/playlist?list=PLyGKBDfnk-iDj3FBd0Avr_dLbrU8VG73O Fall 2006:
http://ocw.mit.edu/courses/brain-and-cognitive-sciences/9-520-statistical-learning-theory-and-applications-spring-2006/

More MIT OCW Statistical Learning Theory

18.465 Spring 2007:
http://ocw.mit.edu/courses/mathematics/18-465-topics-in-statistics-statistical-learning-theory-spring-2007/

Job Boards, Community, News Aggregators:

Kaggle:
https://www.kaggle.com/

Data Tau:
http://www.datatau.com/

Hacker News:
https://news.ycombinator.com/

Glass Door:
https://www.glassdoor.com/index.htm

Implementation and Tutorials:

Tensor Flow Playground:
http://playground.tensorflow.org

ConvNetJS: Deep learning in your web browser (Andrej Karpathy):
http://cs.stanford.edu/people/karpathy/convnetjs/

Scikit-Learn:
Jake Vanderplas PyCon 2015 Sckit-Learn Tutorial:
https://www.youtube.com/watch?v=L7R4HUQ-eQ0

Deep Learning:
Theano:
http://deeplearning.net/tutorial/

Blogs and Blog Posts:

Andrej Karpathy:
Blog: http://karpathy.github.io/

A Hacker's Guide to Neural Networks: http://karpathy.github.io/neuralnets/

Some Notable Names in Research:

https://www.quora.com/Who-are-some-notable-machine-learning-researchers