https://github.com/justmarkham/scikit-learn-videos

https://www.class-central.com/mooc/835/coursera-machine-learning

https://lagunita.stanford.edu/courses/HumanitiesSciences/StatLearning/Winter2016/courseware/f6eb0a2902904c6e8f74a2c15833d1ad/97f87bf36b4a4eeb90e1ca1171fb6750/

https://lagunita.stanford.edu/courses/HumanitiesSciences/StatLearning/Winter2016/ba1951b8f66c4cdca2fcaabcdc91b792/

rWant 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/sci…

The IPython notebook shown in the video is available on GitHub:
https://github.com/justmarkham/scikit…

== RESOURCES ==
Six reasons why I recommend scikit-learn: http://radar.oreilly.com/2013/12/six-…
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-…
scikit-learn installation: http://scikit-learn.org/stable/instal…
Anaconda installation: https://store.continuum.io/cshop/anac…
IPython installation: http://ipython.org/install.html
nbviewer: http://nbviewer.ipython.org/
IPython documentation: http://ipython.org/ipython-doc/stable…
IPython Notebook tutorials: http://nbviewer.ipython.org/github/ip…
Mastering Markdown: https://guides.github.com/features/ma…
Codecademy’s Python course: http://www.codecademy.com/en/tracks/p…
DataQuest: https://dataquest.io/missions
Google’s Python class: https://developers.google.com/edu/pyt…
Python for Informatics: http://www.pythonlearn.com/

== SUBSCRIBE! ==
https://www.youtube.com/user/datascho…

== LET’S CONNECT! ==
Blog: http://www.dataschool.io
Newsletter: http://www.dataschool.io/subscribe/
Twitter: https://twitter.com/justmarkham
GitHub: https://github.com/justmarkham

python lesson iris

Now that we’ve set up Python for machine learning, let’s get started by loading an example dataset into scikit-learn! We’ll explore the famous « iris » dataset, learn some important machine learning terminology, and discuss the four key requirements for working with data in scikit-learn.

This is the third video in the series: « Introduction to machine learning with scikit-learn ». Read more about the video here:
http://blog.kaggle.com/2015/04/22/sci…

The IPython notebook shown in the video is available on GitHub:
https://github.com/justmarkham/scikit…

== RESOURCES ==
Iris dataset in UCI Machine Learning Repository: http://archive.ics.uci.edu/ml/dataset…
scikit-learn dataset loading utilities: http://scikit-learn.org/stable/datasets/
Fast Numerical Computing with NumPy (slides): https://speakerdeck.com/jakevdp/losin…
Fast Numerical Computing with NumPy (video): https://www.youtube.com/watch?v=EEUXK…
Introduction to NumPy (PDF): http://www.engr.ucsb.edu/~shell/che21…

== SUBSCRIBE! ==
https://www.youtube.com/user/datascho…

== LET’S CONNECT! ==
Blog: http://www.dataschool.io
Newsletter: http://www.dataschool.io/subscribe/
Twitter: https://twitter.com/justmarkham
GitHub: https://github.com/justmarkham

Now that we’re familiar with the famous iris dataset, let’s actually use a classification model in scikit-learn to predict the species of an iris! We’ll learn how the K-nearest neighbors (KNN) model works, and then walk through the four steps for model training and prediction in scikit-learn. Finally, we’ll see how easy it is to try out a different classification model, namely logistic regression.

This is the fourth video in the series: « Introduction to machine learning with scikit-learn ». Read more about the video here:
http://blog.kaggle.com/2015/04/30/sci…

The IPython notebook shown in the video is available on GitHub:
https://github.com/justmarkham/scikit…

== RESOURCES ==

Iris dataset in UCI Machine Learning Repository: http://archive.ics.uci.edu/ml/dataset…
Nearest Neighbors user guide: http://scikit-learn.org/stable/module…
KNeighborsClassifier class documentation: http://scikit-learn.org/stable/module…
Logistic Regression user guide: http://scikit-learn.org/stable/module…
LogisticRegression class documentation: http://scikit-learn.org/stable/module…
Videos from An Introduction to Statistical Learning: http://www.dataschool.io/15-hours-of-…

== SUBSCRIBE! ==
https://www.youtube.com/user/datascho…

== LET’S CONNECT! ==
Blog: http://www.dataschool.io
Newsletter: http://www.dataschool.io/subscribe/
Twitter: https://twitter.com/justmarkham
GitHub: https://github.com/justmarkham