2020-07-17 10:19:16

docs/applied-ml/SUMMARY.md

++   [Machine Learning Mastery 应用机器学习教程](README.md)
++   [5竞争机器学习的好处](5-benefits-of-competitive-machine-learning.md)
++   [过度拟合的简单直觉，或者为什么测试训练数据是一个坏主意](a-simple-intuition-for-overfitting.md)
++   [特征选择简介](an-introduction-to-feature-selection.md)
++   [应用机器学习作为一个搜索问题的温和介绍](applied-machine-learning-as-a-search-problem.md)
++   [为什么应用机器学习很难](applied-machine-learning-is-hard.md)
++   [为什么我的结果不如我想的那么好？你可能过度拟合了](arent-results-good-thought-youre-probably-overfitting.md)
++   [用ROC曲线评估和比较分类器表现](assessing-comparing-classifier-performance-roc-curves-2.md)
++   [BigML评论：发现本机学习即服务平台的聪明功能](bigml-review-discover-the-clever-features-in-this-machine-learning-as-a-service-platform.md)
++   [BigML教程：开发您的第一个决策树并进行预测](bigml-tutorial-develop-your-first-decision-tree-and-make-predictions.md)
++   [构建生产机器学习基础设施](building-a-production-machine-learning-infrastructure.md)
++   [分类准确性不够：可以使用更多表现测量](classification-accuracy-is-not-enough-more-performance-measures-you-can-use.md)
++   [一种预测模型的巧妙应用](clever-application-of-a-predictive-model.md)
++   [机器学习项目中常见的陷阱](common-pitfalls-machine-learning-projects.md)
++   [数据清理：将凌乱的数据转换为整洁的数据](data-cleaning-turn-messy-data-into-tidy-data.md)
++   [机器学习中的数据泄漏](data-leakage-machine-learning.md)
++   [数据，学习和建模](data-learning-and-modeling.md)
++   [数据管理至关重要以及为什么需要认真对待它](data-management-matters-and-why-you-need-to-take-it-seriously.md)
++   [将预测模型部署到生产中](deploy-machine-learning-model-to-production.md)
++   [参数和超参数之间有什么区别？](difference-between-a-parameter-and-a-hyperparameter.md)
++   [测试和验证数据集之间有什么区别？](difference-test-validation-datasets.md)
++   [发现特征工程，如何设计特征以及如何获得它](discover-feature-engineering-how-to-engineer-features-and-how-to-get-good-at-it.md)
++   [如何开始使用Kaggle](get-started-with-kaggle.md)
++   [超越预测](going-beyond-predictions.md)
++   [如何在评估机器学习算法时选择正确的测试选项](how-to-choose-the-right-test-options-when-evaluating-machine-learning-algorithms.md)
++   [如何定义机器学习问题](how-to-define-your-machine-learning-problem.md)
++   [如何评估机器学习算法](how-to-evaluate-machine-learning-algorithms.md)
++   [如何获得基线结果及其重要性](how-to-get-baseline-results-and-why-they-matter.md)
++   [如何充分利用机器学习数据](how-to-get-the-most-from-your-machine-learning-data.md)
++   [如何识别数据中的异常值](how-to-identify-outliers-in-your-data.md)
++   [如何提高机器学习效果](how-to-improve-machine-learning-results.md)
++   [如何在竞争机器学习中踢屁股](how-to-kick-ass-in-competitive-machine-learning.md)
++   [如何知道您的机器学习模型是否具有良好的表现](how-to-know-if-your-machine-learning-model-has-good-performance.md)
++   [如何布局和管理您的机器学习项目](how-to-layout-and-manage-your-machine-learning-project.md)
++   [如何为机器学习准备数据](how-to-prepare-data-for-machine-learning.md)
++   [如何减少最终机器学习模型中的方差](how-to-reduce-model-variance.md)
++   [如何使用机器学习结果](how-to-use-machine-learning-results.md)
++   [如何解决像数据科学家这样的问题](how-to-work-through-a-problem-like-a-data-scientist.md)
++   [通过数据预处理提高模型精度](improve-model-accuracy-with-data-pre-processing.md)
++   [处理机器学习的大数据文件的7种方法](large-data-files-machine-learning.md)
++   [建立机器学习系统的经验教训](lessons-learned-building-machine-learning-systems.md)
++   [如何使用机器学习清单可靠地获得准确的预测（即使您是初学者）](machine-learning-checklist.md)
++   [机器学习模型运行期间要做什么](machine-learning-model-running.md)
++   [机器学习表现改进备忘单](machine-learning-performance-improvement-cheat-sheet.md)
++   [来自世界级从业者的机器学习技巧：Phil Brierley](machine-learning-tips-from-a-world-class-practitioner-phil-brierley.md)
++   [模型预测精度与机器学习中的解释](model-prediction-versus-interpretation-in-machine-learning.md)
++   [竞争机器学习的模型选择技巧](model-selection-tips-from-competitive-machine-learning.md)
++   [机器学习需要多少训练数据？](much-training-data-required-machine-learning.md)
++   [如何系统地规划和运行机器学习实验](plan-run-machine-learning-experiments-systematically.md)
++   [应用机器学习过程](process-for-working-through-machine-learning-problems.md)
++   [默认情况下可重现的机器学习结果](reproducible-machine-learning-results-by-default.md)
++   [10个实践应用机器学习的标准数据集](standard-machine-learning-datasets.md)
++   [简单的三步法到最佳机器学习算法](steps-to-the-best-machine-learning-algorithm.md)
++   [打击机器学习数据集中不平衡类的8种策略](tactics-to-combat-imbalanced-classes-in-your-machine-learning-dataset.md)
++   [模型表现不匹配问题（以及如何处理）](the-model-performance-mismatch-problem.md)
++   [黑箱机器学习的诱惑陷阱](the-seductive-trap-of-black-box-machine-learning.md)
++   [如何培养最终的机器学习模型](train-final-machine-learning-model.md)
++   [使用探索性数据分析了解您的问题并获得更好的结果](understand-problem-get-better-results-using-exploratory-data-analysis.md)
++   [什么是数据挖掘和KDD](what-is-data-mining-and-kdd.md)
++   [为什么One-Hot在机器学习中编码数据？](why-one-hot-encode-data-in-machine-learning.md)
++   [为什么你应该在你的机器学习问题上进行抽样检查算法](why-you-should-be-spot-checking-algorithms-on-your-machine-learning-problems.md)
++   [所以，你正在研究机器学习问题......](working-machine-learning-problem.md)
\ No newline at end of file

docs/dl-keras/SUMMARY.md

++   [Machine Learning Mastery Keras 深度学习教程](README.md)
 +   [Keras 中神经网络模型的 5 步生命周期](5-step-life-cycle-neural-network-models-keras.md)
 +   [在 Python 迷你课程中应用深度学习](applied-deep-learning-in-python-mini-course.md)
 +   [Keras 深度学习库的二元分类教程](binary-classification-tutorial-with-the-keras-deep-learning-library.md)

docs/dl-nlp/SUMMARY.md

++   [Machine Learning Mastery 深度学习 NLP 教程](README.md)
 +   [深度学习在自然语言处理中的 7 个应用](applications-of-deep-learning-for-natural-language-processing.md)
 +   [如何实现自然语言处理的波束搜索解码器](beam-search-decoder-natural-language-processing.md)
 +   [深度学习文档分类的最佳实践](best-practices-document-classification-deep-learning.md)

docs/dl-ts/SUMMARY.md

++   [Machine Learning Mastery 深度学习时间序列教程](README.md)
 +   [如何开发人类活动识别的一维卷积神经网络模型](cnn-models-for-human-activity-recognition-time-series-classification.md)
 +   [人类活动识别的深度学习模型](deep-learning-models-for-human-activity-recognition.md)
 +   [如何评估人类活动识别的机器学习算法](evaluate-machine-learning-algorithms-for-human-activity-recognition.md)

docs/linalg/SUMMARY.md

++   [Machine Learning Mastery 线性代数教程](README.md)
++   [机器学习数学符号的基础知识](basics-mathematical-notation-machine-learning.md)
++   [用 NumPy 阵列轻松介绍广播](broadcasting-with-numpy-arrays.md)
++   [如何从 Python 中的 Scratch 计算主成分分析（PCA）](calculate-principal-component-analysis-scratch-python.md)
++   [用于编码器审查的计算线性代数](computational-linear-algebra-coders-review.md)
++   [10 机器学习中的线性代数示例](examples-of-linear-algebra-in-machine-learning.md)
++   [线性代数的温和介绍](gentle-introduction-linear-algebra.md)
++   [用 NumPy 轻松介绍 Python 中的 N 维数组](gentle-introduction-n-dimensional-arrays-python-numpy.md)
++   [机器学习向量的温和介绍](gentle-introduction-vectors-machine-learning.md)
++   [如何在 Python 中为机器学习索引，切片和重塑 NumPy 数组](index-slice-reshape-numpy-arrays-machine-learning-python.md)
++   [机器学习的矩阵和矩阵算法简介](introduction-matrices-machine-learning.md)
++   [温和地介绍机器学习的特征分解，特征值和特征向量](introduction-to-eigendecomposition-eigenvalues-and-eigenvectors.md)
++   [NumPy 对预期价值，方差和协方差的简要介绍](introduction-to-expected-value-variance-and-covariance.md)
++   [机器学习矩阵分解的温和介绍](introduction-to-matrix-decompositions-for-machine-learning.md)
++   [用 NumPy 轻松介绍机器学习的张量](introduction-to-tensors-for-machine-learning.md)
++   [用于机器学习的线性代数中的矩阵类型简介](introduction-to-types-of-matrices-in-linear-algebra.md)
++   [用于机器学习的线性代数备忘单](linear-algebra-cheat-sheet-for-machine-learning.md)
++   [线性代数的深度学习](linear-algebra-for-deep-learning.md)
++   [用于机器学习的线性代数（7 天迷你课程）](linear-algebra-machine-learning-7-day-mini-course.md)
++   [机器学习的线性代数](linear-algebra-machine-learning.md)
++   [机器学习矩阵运算的温和介绍](matrix-operations-for-machine-learning.md)
++   [线性代数评论没有废话指南](no-bullshit-guide-to-linear-algebra-review.md)
++   [学习机器学习线性代数的主要资源](resources-for-linear-algebra-in-machine-learning.md)
++   [浅谈机器学习的奇异值分解](singular-value-decomposition-for-machine-learning.md)
++   [如何用线性代数求解线性回归](solve-linear-regression-using-linear-algebra.md)
++   [用于机器学习的稀疏矩阵的温和介绍](sparse-matrices-for-machine-learning.md)
++   [机器学习中向量规范的温和介绍](vector-norms-machine-learning.md)
++   [学习线性代数用于机器学习的 5 个理由](why-learn-linear-algebra-for-machine-learning.md)
\ No newline at end of file

docs/lstm/SUMMARY.md

++   [Machine Learning Mastery LSTM 教程](README.md)
++   [Keras中长短期记忆模型的5步生命周期](5-step-life-cycle-long-short-term-memory-models-keras.md)
++   [长短时记忆循环神经网络的注意事项](attention-long-short-term-memory-recurrent-neural-networks.md)
++   [CNN长短期记忆网络](cnn-long-short-term-memory-networks.md)
++   [逆向神经网络中的深度学习速成课程](crash-course-recurrent-neural-networks-deep-learning.md)
++   [可变长度输入序列的数据准备](data-preparation-variable-length-input-sequences-sequence-prediction.md)
++   [如何用Keras开发用于Python序列分类的双向LSTM](develop-bidirectional-lstm-sequence-classification-python-keras.md)
++   [如何开发Keras序列到序列预测的编码器 - 解码器模型](develop-encoder-decoder-model-sequence-sequence-prediction-keras.md)
++   [如何诊断LSTM模型的过度拟合和欠拟合](diagnose-overfitting-underfitting-lstm-models.md)
++   [如何开发一种编码器 - 解码器模型，注重Keras中的序列到序列预测](encoder-decoder-attention-sequence-to-sequence-prediction-keras.md)
++   [编码器 - 解码器长短期存储器网络](encoder-decoder-long-short-term-memory-networks.md)
++   [神经网络中爆炸梯度的温和介绍](exploding-gradients-in-neural-networks.md)
++   [对时间反向传播的温和介绍](gentle-introduction-backpropagation-time.md)
++   [生成长短期记忆网络的温和介绍](gentle-introduction-generative-long-short-term-memory-networks.md)
++   [专家对长短期记忆网络的简要介绍](gentle-introduction-long-short-term-memory-networks-experts.md)
++   [在序列预测问题上充分利用LSTM](get-the-most-out-of-lstms.md)
++   [编辑器 - 解码器循环神经网络全局注意的温和介绍](global-attention-for-encoder-decoder-recurrent-neural-networks.md)
++   [如何利用长短时记忆循环神经网络处理很长的序列](handle-long-sequences-long-short-term-memory-recurrent-neural-networks.md)
++   [如何在Python中对一个热编码序列数据](how-to-one-hot-encode-sequence-data-in-python.md)
++   [如何使用编码器 - 解码器LSTM来回显随机整数序列](how-to-use-an-encoder-decoder-lstm-to-echo-sequences-of-random-integers.md)
++   [具有注意力的编码器 - 解码器RNN体系结构的实现模式](implementation-patterns-encoder-decoder-rnn-architecture-attention.md)
++   [学习使用编码器解码器LSTM循环神经网络添加数字](learn-add-numbers-seq2seq-recurrent-neural-networks.md)
++   [如何学习长短时记忆循环神经网络回声随机整数](learn-echo-r​​andom-integers-long-short-term-memory-recurrent-neural-networks.md)
++   [具有Keras的长短期记忆循环神经网络的迷你课程](long-short-term-memory-recurrent-neural-networks-mini-course.md)
++   [LSTM自动编码器的温和介绍](lstm-autoencoders.md)
++   [如何用Keras中的长短期记忆模型进行预测](make-predictions-long-short-term-memory-models-keras.md)
++   [用Python中的长短期内存网络演示内存](memory-in-a-long-short-term-memory-network.md)
++   [基于循环神经网络的序列预测模型的简要介绍](models-sequence-prediction-recurrent-neural-networks.md)
++   [深度学习的循环神经网络算法之旅](recurrent-neural-network-algorithms-for-deep-learning.md)
++   [如何重塑Keras中长短期存储网络的输入数据](reshape-in​​put-data-long-short-term-memory-networks-keras.md)
++   [了解Keras中LSTM的返回序列和返回状态之间的差异](return-sequences-and-return-states-for-lstms-in-keras.md)
++   [RNN展开的温和介绍](rnn-unrolling.md)
++   [5学习LSTM循环神经网络的简单序列预测问题的例子](sequence-prediction-problems-learning-lstm-recurrent-neural-networks.md)
++   [使用序列进行预测](sequence-prediction.md)
++   [堆叠长短期内存网络](stacked-long-short-term-memory-networks.md)
++   [什么是教师强制循环神经网络？](teacher-forcing-for-recurrent-neural-networks.md)
++   [如何在Python中使用TimeDistributed Layer for Long Short-Term Memory Networks](timedistributed-layer-for-long-short-term-memory-networks-in-python.md)
++   [如何准备Keras中截断反向传播的序列预测](truncated-backpropagation-through-time-in-keras.md)
++   [如何在使用LSTM进行训练和预测时使用不同的批量大小](use-different-batch-sizes-training-predicting-python-keras.md)
\ No newline at end of file

docs/ml-algo/SUMMARY.md

++   [Machine Learning Mastery 机器学习算法教程](README.md)
++   [机器学习算法之旅](a-tour-of-machine-learning-algorithms.md)
++   [用于机器学习的装袋和随机森林集合算法](bagging-and-random-forest-ensemble-algorithms-for-machine-learning.md)
++   [从头开始实施机器学习算法的好处](benefits-of-implementing-machine-learning-algorithms-from-scratch.md)
++   [更好的朴素贝叶斯：从朴素贝叶斯算法中获取最多的12个技巧](better-naive-bayes.md)
++   [机器学习的提升和AdaBoost](boosting-and-adaboost-for-machine-learning.md)
++   [选择机器学习算法：Microsoft Azure的经验教训](choosing-machine-learning-algorithms-lessons-from-microsoft-azure.md)
++   [机器学习的分类和回归树](classification-and-regression-trees-for-machine-learning.md)
++   [什么是机器学习中的混淆矩阵](confusion-matrix-machine-learning.md)
++   [如何使用Python从头开始创建算法测试工具](create-algorithm-test-harness-scratch-python.md)
++   [通过创建机器学习算法的目标列表来控制](create-lists-of-machine-learning-algorithms.md)
++   [从头开始停止编码机器学习算法](dont-implement-machine-learning-algorithms.md)
++   [在实现机器学习算法时，不要从开源代码开始](dont-start-with-open-source-code-when-implementing-machine-learning-algorithms.md)
++   [不要使用随机猜测作为基线分类器](dont-use-random-guessing-as-your-baseline-classifier.md)
++   [浅谈机器学习中的概念漂移](gentle-introduction-concept-drift-machine-learning.md)
++   [温和介绍机器学习中的偏差 - 方差权衡](gentle-introduction-to-the-bias-variance-trade-off-in-machine-learning.md)
++   [机器学习的梯度下降](gradient-descent-for-machine-learning.md)
++   [机器学习算法如何工作（他们学习输入到输出的映射）](how-machine-learning-algorithms-work.md)
++   [如何建立机器学习算法的直觉](how-to-build-an-intuition-for-machine-learning-algorithms.md)
++   [如何实现机器学习算法](how-to-implement-a-machine-learning-algorithm.md)
++   [如何研究机器学习算法行为](how-to-investigate-machine-learning-algorithm-behavior.md)
++   [如何学习机器学习算法](how-to-learn-a-machine-learning-algorithm.md)
++   [如何研究机器学习算法](how-to-research-a-machine-learning-algorithm.md)
++   [如何研究机器学习算法](how-to-study-machine-learning-algorithms.md)
++   [如何在Python中从头开始实现反向传播算法](implement-backpropagation-algorithm-scratch-python.md)
++   [如何用Python从头开始实现Bagging](implement-bagging-scratch-python.md)
++   [如何用Python从头开始实现基线机器学习算法](implement-baseline-machine-learning-algorithms-scratch-python.md)
++   [如何在Python中从头开始实现决策树算法](implement-decision-tree-algorithm-scratch-python.md)
++   [如何用Python从头开始实现学习向量量化](implement-learning-vector-quantization-scratch-python.md)
++   [如何利用Python从头开始随机梯度下降实现线性回归](implement-linear-regression-stochastic-gradient-descent-scratch-python.md)
++   [如何利用Python从头开始随机梯度下降实现Logistic回归](implement-logistic-regression-stochastic-gradient-descent-scratch-python.md)
++   [如何用Python从头开始实现机器学习算法表现指标](implement-machine-learning-algorithm-performance-metrics-scratch-python.md)
++   [如何在Python中从头开始实现感知器算法](implement-perceptron-algorithm-scratch-python.md)
++   [如何在Python中从零开始实现随机森林](implement-random-forest-scratch-python.md)
++   [如何在Python中从头开始实现重采样方法](implement-resampling-methods-scratch-python.md)
++   [如何用Python从头开始实现简单线性回归](implement-simple-linear-regression-scratch-python.md)
++   [如何用Python从头开始实现堆栈泛化（Stacking）](implementing-stacking-scratch-python.md)
++   [K-Nearest Neighbors for Machine Learning](k-nearest-neighbors-for-machine-learning.md)
++   [学习机器学习的向量量化](learning-vector-quantization-for-machine-learning.md)
++   [机器学习的线性判别分析](linear-discriminant-analysis-for-machine-learning.md)
++   [机器学习的线性回归](linear-regression-for-machine-learning.md)
++   [使用梯度下降进行机器学习的线性回归教程](linear-regression-tutorial-using-gradient-descent-for-machine-learning.md)
++   [如何在Python中从头开始加载机器学习数据](load-machine-learning-data-scratch-python.md)
++   [机器学习的Logistic回归](logistic-regression-for-machine-learning.md)
++   [机器学习的Logistic回归教程](logistic-regression-tutorial-for-machine-learning.md)
++   [机器学习算法迷你课程](machine-learning-algorithms-mini-course.md)
++   [如何在Python中从头开始实现朴素贝叶斯](naive-bayes-classifier-scratch-python.md)
++   [朴素贝叶斯机器学习](naive-bayes-for-machine-learning.md)
++   [朴素贝叶斯机器学习教程](naive-bayes-tutorial-for-machine-learning.md)
++   [机器学习算法的过拟合和欠拟合](overfitting-and-underfitting-with-machine-learning-algorithms.md)
++   [参数化和非参数机器学习算法](parametric-and-nonparametric-machine-learning-algorithms.md)
++   [理解任何机器学习算法的6个问题](question-to-understand-any-machine-learning-algorithm.md)
++   [在机器学习中拥抱随机性](randomness-in-machine-learning.md)
++   [如何使用Python从头开始扩展机器学习数据](scale-machine-learning-data-scratch-python.md)
++   [机器学习的简单线性回归教程](simple-linear-regression-tutorial-for-machine-learning.md)
++   [有监督和无监督的机器学习算法](supervised-and-unsupervised-machine-learning-algorithms.md)
++   [用于机器学习的支持向量机](support-vector-machines-for-machine-learning.md)
++   [在没有数学背景的情况下理解机器学习算法的5种技术](techniques-to-understand-machine-learning-algorithms-without-the-background-in-mathematics.md)
++   [最好的机器学习算法](the-best-machine-learning-algorithm.md)
++   [教程从头开始在Python中实现k-Nearest Neighbors](tutorial-to-implement-k-nearest-neighbors-in-python-from-scratch.md)
++   [通过从零开始实现它们来理解机器学习算法（以及绕过坏代码的策略）](understand-machine-learning-algorithms-by-implementing-them-from-scratch.md)
++   [使用随机森林：在121个数据集上测试179个分类器](use-random-forest-testing-179-classifiers-121-datasets.md)
++   [为什么从零开始实现机器学习算法](why-implement-a-machine-learning-algorithm-from-scratch.md)
\ No newline at end of file

docs/ml-start/SUMMARY.md

++   [Machine Learning Mastery 机器学习入门教程](README.md)
++   [机器学习入门的四个步骤：初学者入门与实践的自上而下策略](4-steps-to-get-started-in-machine-learning.md)
++   [你应该培养的 5 个机器学习领域](5-machine-learning-areas-you-should-be-cultivating.md)
++   [一种选择机器学习算法的数据驱动方法](a-data-driven-approach-to-machine-learning.md)
++   [机器学习中的分析与数值解](analytical-vs-numerical-solutions-in-machine-learning.md)
++   [应用机器学习是一种精英政治](applied-machine-learning-is-a-meritocracy.md)
++   [机器学习的基本概念](basic-concepts-in-machine-learning.md)
++   [如何成为数据科学家](become-data-scientist.md)
++   [初学者如何在机器学习中弄错](beginners-get-it-wrong.md)
++   [机器学习的最佳编程语言](best-programming-language-for-machine-learning.md)
++   [构建机器学习组合](build-a-machine-learning-portfolio.md)
++   [机器学习中分类与回归的区别](classification-versus-regression-in-machine-learning.md)
++   [评估自己作为数据科学家并利用结果建立惊人的数据科学团队](data-science-skills-simple-method-can-use-evaluate-use-results-build-amazing-data-science-teams.md)
++   [探索 Kaggle 大师的方法论和心态：对 Diogo Ferreira 的采访](discover-the-methodology-and-mindset-of-a-kaggle-master-an-interview-with-diogo-ferreira.md)
++   [扩展机器学习工具并展示掌握](extend-machine-learning-tools.md)
++   [通过寻找地标开始机器学习](find-machine-learning-landmarks.md)
++   [温和地介绍预测建模](gentle-introduction-to-predictive-modeling.md)
++   [通过提供结果在机器学习中获得梦想的工作](get-dream-job-machine-learning-delivering-results.md)
++   [如何开始机器学习：自学蓝图](get-started-machine-learning.md)
++   [开始并在机器学习方面取得进展](get-started-make-progress-machine-learning.md)
++   [应用机器学习的 Hello World](hello-world-of-applied-machine-learning.md)
++   [初学者如何使用小型项目开始机器学习并在 Kaggle 上进行竞争](how-a-beginner-used-small-projects-to-get-started-in-machine-learning-and-compete-on-kaggle.md)
++   [我如何开始机器学习？ （简短版）](how-do-i-get-started-in-machine-learning.md)
++   [我是如何开始机器学习的](how-i-got-started-in-machine-learning.md)
++   [如何在机器学习中取得更好的成绩](how-to-get-better-at-machine-learning.md)
++   [如何从在银行工作到担任 Target 的高级数据科学家](how-to-go-from-working-in-a-bank-to-hired-as-senior-data-scientist-at-target.md)
++   [如何学习任何机器学习工具](how-to-learn-any-machine-learning-tool.md)
++   [使用小型目标项目深入了解机器学习工具](investigate-machine-learning-tools.md)
++   [获得付费申请机器学习](ladder-approach-to-becoming-a-machine-learning-consultant.md)
++   [映射机器学习工具的景观](list-machine-learning-tools.md)
++   [机器学习开发环境](machine-learning-development-environment.md)
++   [机器学习金钱](machine-learning-for-money.md)
++   [程序员的机器学习](machine-learning-for-programmers.md)
++   [机器学习很有意思](machine-learning-is-fascinating.md)
++   [机器学习是 Kaggle 比赛](machine-learning-is-kaggle-competitions.md)
++   [机器学习现在很受欢迎](machine-learning-is-popular.md)
++   [机器学习掌握方法](machine-learning-mastery-method.md)
++   [机器学习很重要](machine-learning-matters.md)
++   [机器学习 Q＆amp; A：概念漂移，更好的结果和学习更快](machine-learning-qa-concept-drift-better-results-and-learning-faster.md)
++   [缺乏自学机器学习的路线图](machine-learning-roadmap-your-self-study-guide-to-machine-learning.md)
++   [机器学习很重要](machine-learning-that-matters.md)
++   [快速了解任何机器学习工具（即使您是初学者）](machine-learning-tool-templates.md)
++   [机器学习工具](machine-learning-tools.md)
++   [找到你的机器学习部落](machine-learning-tribe.md)
++   [机器学习在一年](machine-learning-year.md)
++   [通过竞争一致的大师 Kaggle](master-kaggle-by-competing-consistently.md)
++   [5 程序员在机器学习中开始犯错误](mistakes-programmers-make-when-starting-in-machine-learning.md)
++   [哲学毕业生到机器学习从业者（Brian Thomas 采访）](philosophy-graduate-to-machine-learning-practitioner.md)
++   [机器学习入门的实用建议](practical-advice-for-getting-started-in-machine-learning.md)
++   [实用机器学习问题](practical-machine-learning-problems.md)
++   [使用来自 UCI 机器学习库的数据集练习机器学习](practice-machine-learning-with-small-in-memory-datasets-from-the-uci-machine-learning-repository.md)
++   [使用秘籍的任何机器学习工具快速启动](proceduralize-machine-learning-tools.md)
++   [程序员可以进入机器学习](programmers-can-get-into-machine-learning.md)
++   [程序员应该进入机器学习](programmers-should-get-into-machine-learning.md)
++   [项目焦点：Shashank Singh 的人脸识别](project-spotlight-face-recognition-with-shashank-singh.md)
++   [项目焦点：使用 Mahout 和 Konstantin Slisenko 进行堆栈交换群集](project-spotlight-stack-exchange-clustering-using-mahout-with-konstantin-slisenko.md)
++   [机器学习自学指南](self-study-guide-to-machine-learning.md)
++   [4 个自学机器学习项目](self-study-machine-learning-projects.md)
++   [ÁlvaroLemos 如何在数据科学团队中获得机器学习实习](student-got-machine-learning-internship-job-data-science-team.md)
++   [如何思考机器学习](think-machine-learning.md)
++   [现实世界机器学习问题之旅](tour-of-real-world-machine-learning-problems.md)
++   [有关机器学习的有用知识](useful-things-to-know-about-machine-learning.md)
++   [如果我没有学位怎么办？](what-if-i-dont-have-a-degree.md)
++   [如果我不是一个优秀的程序员怎么办？](what-if-im-not-a-good-programmer.md)
++   [如果我不擅长数学怎么办？](what-if-im-not-good-at-mathematics.md)
++   [为什么机器学习算法会处理以前从未见过的数据？](what-is-generalization-in-machine-learning.md)
++   [是什么阻碍了你的机器学习目标？](what-is-holding-you-back-from-your-machine-learning-goals.md)
++   [什么是机器学习？](what-is-machine-learning.md)
++   [机器学习适合哪里？](where-does-machine-learning-fit-in.md)
++   [为什么要进入机器学习？](why-get-into-machine-learning.md)
++   [研究对您来说很重要的机器学习问题](work-on-machine-learning-problems-that-matter-to-you.md)
++   [你这样做是错的。为什么机器学习不必如此困难](youre-wrong-machine-learning-not-hard.md)
\ No newline at end of file

docs/sklearn/SUMMARY.md

++   [Machine Learning Mastery Sklearn 教程](README.md)
++   [Scikit-Learn 的温和介绍：Python 机器学习库](a-gentle-introduction-to-scikit-learn-a-python-machine-learning-library.md)
++   [使用 Python 管道和 scikit-learn 自动化机器学习工作流程](automate-machine-learning-workflows-pipelines-python-scikit-learn.md)
++   [如何以及何时使用带有 scikit-learn 的校准分类模型](calibrated-classification-model-in-scikit-learn.md)
++   [如何比较 Python 中的机器学习算法与 scikit-learn](compare-machine-learning-algorithms-python-scikit-learn.md)
++   [用于机器学习开发人员的 Python 崩溃课程](crash-course-python-machine-learning-developers.md)
++   [用 scikit-learn 在 Python 中集成机器学习算法](ensemble-machine-learning-algorithms-python-scikit-learn.md)
++   [使用重采样评估 Python 中机器学习算法的表现](evaluate-performance-machine-learning-algorithms-python-using-resampling.md)
++   [使用 Scikit-Learn 在 Python 中进行特征选择](feature-selection-in-python-with-scikit-learn.md)
++   [Python 中机器学习的特征选择](feature-selection-machine-learning-python.md)
++   [如何使用 scikit-learn 在 Python 中生成测试数据集](generate-test-datasets-python-scikit-learn.md)
++   [scikit-learn 中的机器学习算法秘籍](get-your-hands-dirty-with-scikit-learn-now.md)
++   [如何使用 Python 处理丢失的数据](handle-missing-data-python.md)
++   [如何开始使用 Python 进行机器学习](how-to-get-started-with-machine-learning-in-python.md)
++   [如何使用 Scikit-Learn 在 Python 中加载数据](how-to-load-data-in-python-with-scikit-learn.md)
++   [Python 中概率评分方法的简要介绍](how-to-score-probability-predictions-in-python.md)
++   [如何用 Scikit-Learn 调整算法参数](how-to-tune-algorithm-parameters-with-scikit-learn.md)
++   [如何在 Mac OS X 上安装 Python 3 环境以进行机器学习和深度学习](install-python-3-environment-mac-os-x-machine-learning-deep-learning.md)
++   [使用 scikit-learn 进行机器学习简介](introduction-machine-learning-scikit-learn.md)
++   [从 shell 到一本带有 Fernando Perez 单一工具的书的 IPython](ipython-from-the-shell-to-a-book-with-a-single-tool-with-fernando-perez.md)
++   [如何使用 Python 3 为机器学习开发创建 Linux 虚拟机](linux-virtual-machine-machine-learning-development-python-3.md)
++   [如何在 Python 中加载机器学习数据](load-machine-learning-data-python.md)
++   [您在 Python 中的第一个机器学习项目循序渐进](machine-learning-in-python-step-by-step.md)
++   [如何使用 scikit-learn 进行预测](make-predictions-scikit-learn.md)
++   [用于评估 Python 中机器学习算法的度量标准](metrics-evaluate-machine-learning-algorithms-python.md)
++   [使用 Pandas 为 Python 中的机器学习准备数据](prepare-data-for-machine-learning-in-python-with-pandas.md)
++   [如何使用 Scikit-Learn 为 Python 机器学习准备数据](prepare-data-machine-learning-python-scikit-learn.md)
++   [项目焦点：使用 Artem Yankov 在 Python 中进行事件推荐](project-spotlight-with-artem-yankov.md)
++   [用于机器学习的 Python 生态系统](python-ecosystem-machine-learning.md)
++   [Python 是应用机器学习的成长平台](python-growing-platform-applied-machine-learning.md)
++   [Python 机器学习书籍](python-machine-learning-books.md)
++   [Python 机器学习迷你课程](python-machine-learning-mini-course.md)
++   [使用 Pandas 快速和肮脏的数据分析](quick-and-dirty-data-analysis-with-pandas.md)
++   [使用 Scikit-Learn 重新调整 Python 中的机器学习数据](rescaling-data-for-machine-learning-in-python-with-scikit-learn.md)
++   [如何以及何时使用 ROC 曲线和精确调用曲线进行 Python 分类](roc-curves-and-precision-recall-curves-for-classification-in-python.md)
++   [使用 scikit-learn 在 Python 中保存和加载机器学习模型](save-load-machine-learning-models-python-scikit-learn.md)
++   [scikit-learn Cookbook 书评](scikit-learn-cookbook-book-review.md)
++   [如何使用 Anaconda 为机器学习和深度学习设置 Python 环境](setup-python-environment-machine-learning-deep-learning-anaconda.md)
++   [使用 scikit-learn 在 Python 中进行 Spot-Check 分类机器学习算法](spot-check-classification-machine-learning-algorithms-python-scikit-learn.md)
++   [如何在 Python 中开发可重复使用的抽样检查算法框架](spot-check-machine-learning-algorithms-in-python.md)
++   [使用 scikit-learn 在 Python 中进行 Spot-Check 回归机器学习算法](spot-check-regression-machine-learning-algorithms-python-scikit-learn.md)
++   [使用 Python 中的描述性统计来了解您的机器学习数据](understand-machine-learning-data-descriptive-statistics-python.md)
++   [使用 OpenCV，Python 和模板匹配来播放“哪里是 Waldo？”](using-opencv-python-and-template-matching-to-play-wheres-waldo.md)
++   [使用 Pandas 在 Python 中可视化机器学习数据](visualize-machine-learning-data-python-pandas.md)
\ No newline at end of file

docs/stat/SUMMARY.md

++   [Machine Learning Mastery 统计学教程](README.md)
++   [浅谈计算正态汇总统计量](a-gentle-introduction-to-calculating-normal-summary-statistics.md)
++   [非参数统计的温和介绍](a-gentle-introduction-to-nonparametric-statistics.md)
++   [Python中常态测试的温和介绍](a-gentle-introduction-to-normality-tests-in-python.md)
++   [浅谈Bootstrap方法](a-gentle-introduction-to-the-bootstrap-method.md)
++   [浅谈机器学习的中心极限定理](a-gentle-introduction-to-the-central-limit-theorem-for-machine-learning.md)
++   [浅谈机器学习中的大数定律](a-gentle-introduction-to-the-law-of-large-numbers-in-machine-learning.md)
++   [机器学习的所有统计数据](all-of-statistics-for-machine-learning.md)
++   [如何计算Python中机器学习结果的Bootstrap置信区间](calculate-bootstrap-confidence-intervals-machine-learning-results-python.md)
++   [浅谈机器学习的Chi-Squared测试](chi-squared-test-for-machine-learning.md)
++   [机器学习的置信区间](confidence-intervals-for-machine-learning.md)
++   [随机化在机器学习中解决混杂变量的作用](confounding-variables-in-machine-learning.md)
++   [机器学习中的受控实验](controlled-experiments-in-machine-learning.md)
++   [机器学习统计学速成班](crash-course-statistics-machine-learning.md)
++   [统计假设检验的关键值以及如何在Python中计算它们](critical-values-for-statistical-hypothesis-testing.md)
++   [如何在机器学习中谈论数据（统计学和计算机科学术语）](data-terminology-in-machine-learning.md)
++   [Python中数据可视化方法的简要介绍](data-visualization-methods-in-python.md)
++   [Python中效果大小度量的温和介绍](effect-size-measures-in-python.md)
++   [估计随机机器学习算法的实验重复次数](estimate-number-experiment-repeats-stochastic-machine-learning-algorithms.md)
++   [机器学习评估统计的温和介绍](estimation-statistics-for-machine-learning.md)
++   [如何计算Python中的非参数秩相关性](how-to-calculate-nonparametric-rank-correlation-in-python.md)
++   [如何在Python中计算数据的5位数摘要](how-to-calculate-the-5-number-summary-for-your-data-in-python.md)
++   [如何在Python中从头开始编写学生t检验](how-to-code-the-students-t-test-from-scratch-in-python.md)
++   [如何在Python中生成随机数](how-to-generate-random-numbers-in-python.md)
++   [如何转换数据以更好地拟合正态分布](how-to-transform-data-to-fit-the-normal-distribution.md)
++   [如何使用相关来理解变量之间的关系](how-to-use-correlation-to-understand-the-relationship-between-variables.md)
++   [如何使用统计信息识别数据中的异常值](how-to-use-statistics-to-identify-outliers-in-data.md)
++   [用于Python机器学习的随机数生成器简介](introduction-to-random-number-generators-for-machine-learning.md)
++   [k-fold交叉验证的温和介绍](k-fold-cross-validation.md)
++   [如何计算McNemar的比较两种机器学习量词的测试](mcnemars-test-for-machine-learning.md)
++   [Python中非参数统计显着性测试简介](nonparametric-statistical-significance-tests-in-python.md)
++   [如何在Python中使用参数统计显着性测试](parametric-statistical-significance-tests-in-python.md)
++   [机器学习的预测间隔](prediction-intervals-for-machine-learning.md)
++   [应用统计学与机器学习的密切关系](relationship-between-applied-statistics-and-machine-learning.md)
++   [如何使用置信区间报告分类器表现](report-classifier-performance-confidence-intervals.md)
++   [统计数据分布的简要介绍](statistical-data-distributions.md)
++   [15 Python中的统计假设检验（备忘单）](statistical-hypothesis-tests-in-python-cheat-sheet.md)
++   [统计假设检验的温和介绍](statistical-hypothesis-tests.md)
++   [10如何在机器学习项目中使用统计方法的示例](statistical-methods-in-an-applied-machine-learning-project.md)
++   [Python中统计功效和功耗分析的简要介绍](statistical-power-and-power-analysis-in-python.md)
++   [统计抽样和重新抽样的简要介绍](statistical-sampling-and-resampling.md)
++   [比较机器学习算法的统计显着性检验](statistical-significance-tests-for-comparing-machine-learning-algorithms.md)
++   [机器学习中统计容差区间的温和介绍](statistical-tolerance-intervals-in-machine-learning.md)
++   [机器学习统计书籍](statistics-books-for-machine-learning.md)
++   [评估机器学习模型的统计数据](statistics-for-evaluating-machine-learning-models.md)
++   [机器学习统计（7天迷你课程）](statistics-for-machine-learning-mini-course.md)
++   [用于机器学习的简明英语统计](statistics-in-plain-english-for-machine-learning.md)
++   [如何使用统计显着性检验来解释机器学习结果](use-statistical-significance-tests-interpret-machine-learning-results.md)
++   [什么是统计（为什么它在机器学习中很重要）？](what-is-statistics.md)
\ No newline at end of file

docs/ts-intro/SUMMARY.md

++   [Machine Learning Mastery 时间序列入门教程](README.md)
 +   [如何在 Python 中为时间序列预测创建 ARIMA 模型](arima-for-time-series-forecasting-with-python.md)
 +   [用 Python 进行时间序列预测的自回归模型](autoregression-models-time-series-forecasting-python.md)
 +   [如何回溯机器学习模型的时间序列预测](backtest-machine-learning-models-time-series-forecasting.md)

docs/xgboost/SUMMARY.md

++   [Machine Learning Mastery XGBoost 教程](README.md)
 +   [通过在 Python 中使用 XGBoost 提前停止来避免过度拟合](avoid-overfitting-by-early-stopping-with-xgboost-in-python.md)
 +   [如何在 Python 中调优 XGBoost 的多线程支持](best-tune-multithreading-support-xgboost-python.md)
 +   [如何配置梯度提升算法](configure-gradient-boosting-algorithm.md)