Lecture 01 - The Learning ProblemThe Learning Problem - Introduction; supervised, unsupervised, and reinforcement learning. Components of the learning problem. Lecture 1 of 18 of Caltech's Machine Learning Course - CS 156 by Professor Yaser Abu-Mostafa. View course materials in iTunes U Course App - https://itunesu.itunes.apple.com/audit/CODBABB3ZC and on the course website - http://work.caltech.edu/telecourse.html
Produced in association with Caltech Academic Media Technologies under the Attribution-NonCommercial-NoDerivs Creative Commons License (CC BY-NC-ND). To learn more about this license, http://creativecommons.org/licenses/by-nc-nd/3.0/
This lecture was recorded on April 3, 2012, in Hameetman Auditorium at Caltech, Pasadena, CA, USA.
Setting up Python for machine learning: scikit-learn and IPython NotebookWant to get started with machine learning in Python? I'll discuss the pros and cons of the scikit-learn library, show how to install my preferred Python distribution, and demonstrate the basic functionality of the IPython Notebook. If you don't yet know any Python, I'll also provide four recommended resources for learning Python.
This is the second video in the series: "Introduction to machine learning with scikit-learn". Read more about this video here:
http://blog.kaggle.com/2015/04/15/scikit-learn-video-2-setting-up-python-for-machine-learning/
The IPython notebook shown in the video is available on GitHub:
https://github.com/justmarkham/scikit-learn-videos
== RESOURCES ==
Six reasons why I recommend scikit-learn: http://radar.oreilly.com/2013/12/six-reasons-why-i-recommend-scikit-learn.html
API design for machine learning software: http://arxiv.org/pdf/1309.0238v1.pdf
Should you teach Python or R for data science?: http://www.dataschool.io/python-or-r-for-data-science/
scikit-learn installation: http://scikit-learn.org/stable/install.html
Anaconda installation: https://store.continuum.io/cshop/anaconda/
IPython installation: http://ipython.org/install.html
nbviewer: http://nbviewer.ipython.org/
IPython documentation: http://ipython.org/ipython-doc/stable/index.html
IPython Notebook tutorials: http://nbviewer.ipython.org/github/ipython/ipython/blob/master/examples/Notebook/Index.ipynb
Mastering Markdown: https://guides.github.com/features/mastering-markdown/
Codecademy's Python course: http://www.codecademy.com/en/tracks/python
DataQuest: https://dataquest.io/missions
Google's Python class: https://developers.google.com/edu/python/
Python for Informatics: http://www.pythonlearn.com/
