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I'm using this theme in Gmail on the Web and I think you'll like it too! Check it out on your desktop or laptop. #gmailthemes

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Outstanding tutorial.

Part 1: Statistical Data Analysis in Python, SciPy2013 Tutorial, Part 1 of 4

Part 2: Statistical Data Analysis in Python, SciPy2013 Tutorial, Part 2 of 4

Part 3: Statistical Data Analysis in Python, SciPy2013 Tutorial, Part 3 of 4

Part 4: Statistical Data Analysis in Python, SciPy2013 Tutorial, Part 4 of 4

If you know Pandas reasonably well, you may want to watch only the fourth video.

Ipython notebooks here: https://github.com/fonnesbeck/statistical-analysis-python-tutorial

**Statistical Data Analysis in Python, SciPy2013 Tutorial***Presenter: Christopher Fonnesbeck*Part 1: Statistical Data Analysis in Python, SciPy2013 Tutorial, Part 1 of 4

Part 2: Statistical Data Analysis in Python, SciPy2013 Tutorial, Part 2 of 4

Part 3: Statistical Data Analysis in Python, SciPy2013 Tutorial, Part 3 of 4

Part 4: Statistical Data Analysis in Python, SciPy2013 Tutorial, Part 4 of 4

If you know Pandas reasonably well, you may want to watch only the fourth video.

Ipython notebooks here: https://github.com/fonnesbeck/statistical-analysis-python-tutorial

*This tutorial will introduce the use of Python for statistical data analysis, using data stored as Pandas DataFrame objects. Much of the work involved in analyzing data resides in importing, cleaning and transforming data in preparation for analysis. Therefore, the first half of the course is comprised of a 2-part overview of basic and intermediate Pandas usage that will show how to effectively manipulate datasets in memory. This includes tasks like indexing, alignment, join/merge methods, date/time types, and handling of missing data. Next, we will cover plotting and visualization using Pandas and Matplotlib, focusing on creating effective visual representations of your data, while avoiding common pitfalls. Finally, participants will be introduced to methods for statistical data modeling using some of the advanced functions in Numpy, Scipy and Pandas. This will include fitting your data to probability distributions, estimating relationships among variables using linear and non-linear models, and a brief introduction to Bayesian methods. Each section of the tutorial will involve hands-on manipulation and analysis of sample datasets, to be provided to attendees in advance.* Post has shared content

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Amazing tutorial on scikit-learn. More than 7 hours of machine learning goodness.

Presenters: Gaël Varoquaux, Jake Vanderplas, Olivier Grisel

Session I:

Intro to scikit-learn (I), SciPy2013 Tutorial, Part 1 of 3

Intro to scikit-learn (I), SciPy2013 Tutorial, Part 2 of 3

Intro to scikit-learn (I), SciPy2013 Tutorial, Part 3 of 3

Session 2:

Intro to scikit-learn (II), SciPy2013 Tutorial, Part 1 of 2

Intro to scikit-learn (II), SciPy2013 Tutorial, Part 2 of 2

Notebooks here: https://github.com/jakevdp/sklearn_scipy2013

**Intro to scikit-learn, SciPy2013 Tutorial**Presenters: Gaël Varoquaux, Jake Vanderplas, Olivier Grisel

Session I:

Intro to scikit-learn (I), SciPy2013 Tutorial, Part 1 of 3

Intro to scikit-learn (I), SciPy2013 Tutorial, Part 2 of 3

Intro to scikit-learn (I), SciPy2013 Tutorial, Part 3 of 3

Session 2:

Intro to scikit-learn (II), SciPy2013 Tutorial, Part 1 of 2

Intro to scikit-learn (II), SciPy2013 Tutorial, Part 2 of 2

*Session I will assume participants already have a basic knowledge of using numpy and matplotlib for manipulating and visualizing data. It will require no prior knowledge of machine learning or scikit-learn. The goals of Session I are to introduce participants to the basic concepts of machine learning, to give a hands-on introduction to using Scikit-learn for machine learning in Python, and give participants experience with several practical examples and applications of applying supervised learning to a variety of data. It will cover basic classification and regression problems, regularization of learning models, basic cross-validation, and some examples from text mining and image processing, all using the tools available in scikit-learn.**Session II will build upon Session I, and assume familiarity with the concepts covered there. The goals of Session II are to introduce more involved algorithms and techniques which are vital for successfully applying machine learning in practice. It will cover cross-validation and hyperparameter optimization, unsupervised algorithms, pipelines, and go into depth on a few extremely powerful learning algorithms available in Scikit-learn: Support Vector Machines, Random Forests, and Sparse Models. We will finish with an extended exercise applying scikit-learn to a real-world problem.*Notebooks here: https://github.com/jakevdp/sklearn_scipy2013

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Strongly recommend.

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I'll post the answer in the comments within a few days. Don't spoil it for everyone else!

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