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.
Intro to scikit-learn, SciPy2013 Tutorial
Presenters: Gaël Varoquaux, Jake Vanderplas, Olivier Grisel
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
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
Active appearance model - Wikipedia, the free encyclopedia
Active appearance model. From Wikipedia, the free encyclopedia. Jump to: navigation, search. An active appearance model (AAM) is a computer
MFC单文档/多视图常见错误：error C2143: syntax error : missing ';' before '*' - qian...
今天，想做一个MFC单文档多视图的应用程序界面，可是开始建好工程按照多视图的要求设置好后，编译却发现以下错误，找了好久，郁闷了好久也没找出来，最后在网上找到如下解决方案，才得以脱身，真是万分感谢，收藏！ 原文如下：error C2143: syntax error : missi