# 關于科學數據處理的統計學習教程 Statistical learning [Machine learning](https://en.wikipedia.org/wiki/Machine_learning) is a technique with a growing importance, as the size of the datasets experimental sciences are facing is rapidly growing. Problems it tackles range from building a prediction function linking different observations, to classifying observations, or learning the structure in an unlabeled dataset. This tutorial will explore *statistical learning*, the use of machine learning techniques with the goal of [statistical inference](https://en.wikipedia.org/wiki/Statistical_inference): drawing conclusions on the data at hand. Scikit-learn is a Python module integrating classic machine learning algorithms in the tightly-knit world of scientific Python packages ([NumPy](http://www.scipy.org), [SciPy](http://www.scipy.org), [matplotlib](http://matplotlib.org)). - [機器學習: scikit-learn 中的設置以及預估對象](settings.html) - [數據集](settings.html#id1) - [預估對象](settings.html#id2) - [監督學習：從高維觀察預測輸出變量](supervised_learning.html) - [最近鄰和維度懲罰](supervised_learning.html#id2) - [線性模型：從回歸到稀疏](supervised_learning.html#id6) - [支持向量積(SVMs)](supervised_learning.html#svms) - [模型選擇：選擇估計量及其參數](model_selection.html) - [分數和交叉驗證分數](model_selection.html#id2) - [交叉驗證生成器](model_selection.html#cv-generators-tut) - [網格搜索和交叉驗證估計量](model_selection.html#id4) - [無監督學習: 尋求數據表示](unsupervised_learning.html) - [聚類: 對樣本數據進行分組](unsupervised_learning.html#id2) - [分解: 將一個信號轉換成多個成份并且加載](unsupervised_learning.html#id6) - [把它們放在一起](putting_together.html) - [模型管道化](putting_together.html#id2) - [用特征面進行人臉識別](putting_together.html#id3) - [開放性問題: 股票市場結構](putting_together.html#id4) - [尋求幫助](finding_help.html) - [項目郵件列表](finding_help.html#id2) - [機器學習從業者的 Q&A 社區](finding_help.html#q-a)