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* [数据类型](数据类型/data-type.md) | ||
* [基本统计](基本统计/summary-statistics.md) | ||
* [summary statistics(概括统计)](基本统计/summary-statistics.md) | ||
* [correlations(相关性系数)](基本统计/correlations.md) | ||
* [tratified sampling(分层取样)](基本统计/tratified-sampling.md) | ||
* [hypothesis testing(假设检验)](基本统计/hypothesis-testing.md) | ||
* [random data generation(随机数生成)](基本统计/random-data-generation.md) | ||
* [Kernel density estimation(核密度估计)](基本统计/kernel-density-estimation.md) | ||
* [协同过滤](推荐/交换最小二乘/ALS.md) | ||
* [交换最小二乘](推荐/交换最小二乘/ALS.md) | ||
* [分类和回归](分类和回归/readme.md) | ||
* [线性模型](分类和回归/线性模型/readme.md) | ||
* [SVMs(支持向量机)](分类和回归/线性模型/支持向量机/lsvm.md) | ||
* [逻辑回归](分类和回归/线性模型/逻辑回归/logic-regression.md) | ||
* [线性回归](分类和回归/线性模型/回归/regression.md) | ||
* [朴素贝叶斯](分类和回归/朴素贝叶斯/nb.md) | ||
* [决策树](分类和回归/决策树/decision-tree.md) | ||
* [组合树](分类和回归/组合树/readme.md) | ||
* [随机森林](分类和回归/组合树/随机森林/random-forests.md) | ||
* [梯度提升树](分类和回归/组合树/梯度提升树/gbts.md) | ||
* [保序回归](分类和回归/保序回归/isotonic-regression.md) | ||
* [聚类](聚类/readme.md) | ||
* [k-means算法](聚类/k-means/k-means.md) | ||
* [GMM(高斯混合模型)](聚类/gaussian-mixture/gaussian-mixture.md) | ||
* [PIC(快速迭代聚类)](聚类/PIC/pic.md) | ||
* [LDA(隐式狄利克雷分布)](聚类/LDA/lda.md) | ||
* [二分k-means算法](聚类/bis-k-means/bisecting-k-means.md) | ||
* [流式k-means算法](聚类/streaming-k-means/streaming-k-means.md) | ||
* [最优化算法](最优化算法/梯度下降/gradient-descent.md) | ||
* [梯度下降算法](最优化算法/梯度下降/gradient-descent.md) | ||
* [L-BFGS(限制内存BFGS)](最优化算法/L-BFGS/lbfgs.md) | ||
* [NNLS(非负最小二乘)](最优化算法/非负最小二乘/NNLS.md) | ||
* [降维](降维/SVD/svd.md) | ||
* [EVD(特征值分解)](降维/EVD/evd.md) | ||
* [SVD(奇异值分解)](降维/SVD/svd.md) | ||
* [PCA(主成分分析)](降维/PCA/pca.md) | ||
* [特征抽取和转换](特征抽取和转换/TF-IDF.md) | ||
* [TF-IDF](特征抽取和转换/TF-IDF.md) | ||
* [Word2Vec](特征抽取和转换/Word2Vector.md) | ||
* [StandardScaler(特征缩放)](特征抽取和转换/StandardScaler.md) | ||
* [Normalizer(规则化)](特征抽取和转换/normalizer.md) | ||
* [ChiSqSelector(卡方选择器)](特征抽取和转换/chi-square-selector.md) | ||
* [ElementwiseProduct(元素智能乘积)](特征抽取和转换/element-wise-product.md) |