+   [Machine Learning Mastery 应用机器学习教程](docs/applied-ml/README.md)
    +   [5竞争机器学习的好处](docs/applied-ml/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](docs/applied-ml/a-simple-intuition-for-overfitting.md)
    +   [特征选择简介](docs/applied-ml/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](docs/applied-ml/applied-machine-learning-as-a-search-problem.md)
    +   [为什么应用机器学习很难](docs/applied-ml/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](docs/applied-ml/arent-results-good-thought-youre-probably-overfitting.md)
    +   [用ROC曲线评估和比较分类器表现](docs/applied-ml/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](docs/applied-ml/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](docs/applied-ml/bigml-tutorial-develop-your-first-decision-tree-and-make-predictions.md)
    +   [构建生产机器学习基础设施](docs/applied-ml/building-a-production-machine-learning-infrastructure.md)
    +   [分类准确性不够：可以使用更多表现测量](docs/applied-ml/classification-accuracy-is-not-enough-more-performance-measures-you-can-use.md)
    +   [一种预测模型的巧妙应用](docs/applied-ml/clever-application-of-a-predictive-model.md)
    +   [机器学习项目中常见的陷阱](docs/applied-ml/common-pitfalls-machine-learning-projects.md)
    +   [数据清理：将凌乱的数据转换为整洁的数据](docs/applied-ml/data-cleaning-turn-messy-data-into-tidy-data.md)
    +   [机器学习中的数据泄漏](docs/applied-ml/data-leakage-machine-learning.md)
    +   [数据，学习和建模](docs/applied-ml/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](docs/applied-ml/data-management-matters-and-why-you-need-to-take-it-seriously.md)
    +   [将预测模型部署到生产中](docs/applied-ml/deploy-machine-learning-model-to-production.md)
    +   [参数和超参数之间有什么区别？](docs/applied-ml/difference-between-a-parameter-and-a-hyperparameter.md)
    +   [测试和验证数据集之间有什么区别？](docs/applied-ml/difference-test-validation-datasets.md)
    +   [发现特征工程，如何设计特征以及如何获得它](docs/applied-ml/discover-feature-engineering-how-to-engineer-features-and-how-to-get-good-at-it.md)
    +   [如何开始使用Kaggle](docs/applied-ml/get-started-with-kaggle.md)
    +   [超越预测](docs/applied-ml/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](docs/applied-ml/how-to-choose-the-right-test-options-when-evaluating-machine-learning-algorithms.md)
    +   [如何定义机器学习问题](docs/applied-ml/how-to-define-your-machine-learning-problem.md)
    +   [如何评估机器学习算法](docs/applied-ml/how-to-evaluate-machine-learning-algorithms.md)
    +   [如何获得基线结果及其重要性](docs/applied-ml/how-to-get-baseline-results-and-why-they-matter.md)
    +   [如何充分利用机器学习数据](docs/applied-ml/how-to-get-the-most-from-your-machine-learning-data.md)
    +   [如何识别数据中的异常值](docs/applied-ml/how-to-identify-outliers-in-your-data.md)
    +   [如何提高机器学习效果](docs/applied-ml/how-to-improve-machine-learning-results.md)
    +   [如何在竞争机器学习中踢屁股](docs/applied-ml/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](docs/applied-ml/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](docs/applied-ml/how-to-layout-and-manage-your-machine-learning-project.md)
    +   [如何为机器学习准备数据](docs/applied-ml/how-to-prepare-data-for-machine-learning.md)
    +   [如何减少最终机器学习模型中的方差](docs/applied-ml/how-to-reduce-model-variance.md)
    +   [如何使用机器学习结果](docs/applied-ml/how-to-use-machine-learning-results.md)
    +   [如何解决像数据科学家这样的问题](docs/applied-ml/how-to-work-through-a-problem-like-a-data-scientist.md)
    +   [通过数据预处理提高模型精度](docs/applied-ml/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](docs/applied-ml/large-data-files-machine-learning.md)
    +   [建立机器学习系统的经验教训](docs/applied-ml/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）](docs/applied-ml/machine-learning-checklist.md)
    +   [机器学习模型运行期间要做什么](docs/applied-ml/machine-learning-model-running.md)
    +   [机器学习表现改进备忘单](docs/applied-ml/machine-learning-performance-improvement-cheat-sheet.md)
    +   [来自世界级从业者的机器学习技巧：Phil Brierley](docs/applied-ml/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](docs/applied-ml/model-prediction-versus-interpretation-in-machine-learning.md)
    +   [竞争机器学习的模型选择技巧](docs/applied-ml/model-selection-tips-from-competitive-machine-learning.md)
    +   [机器学习需要多少训练数据？](docs/applied-ml/much-training-data-required-machine-learning.md)
    +   [如何系统地规划和运行机器学习实验](docs/applied-ml/plan-run-machine-learning-experiments-systematically.md)
    +   [应用机器学习过程](docs/applied-ml/process-for-working-through-machine-learning-problems.md)
    +   [默认情况下可重现的机器学习结果](docs/applied-ml/reproducible-machine-learning-results-by-default.md)
    +   [10个实践应用机器学习的标准数据集](docs/applied-ml/standard-machine-learning-datasets.md)
    +   [简单的三步法到最佳机器学习算法](docs/applied-ml/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](docs/applied-ml/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）](docs/applied-ml/the-model-performance-mismatch-problem.md)
    +   [黑箱机器学习的诱惑陷阱](docs/applied-ml/the-seductive-trap-of-black-box-machine-learning.md)
    +   [如何培养最终的机器学习模型](docs/applied-ml/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](docs/applied-ml/understand-problem-get-better-results-using-exploratory-data-analysis.md)
    +   [什么是数据挖掘和KDD](docs/applied-ml/what-is-data-mining-and-kdd.md)
    +   [为什么One-Hot在机器学习中编码数据？](docs/applied-ml/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](docs/applied-ml/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......](docs/applied-ml/working-machine-learning-problem.md)
+   [Machine Learning Mastery Keras 深度学习教程](docs/dl-keras/README.md)
    +   [Keras 中神经网络模型的 5 步生命周期](docs/dl-keras/5-step-life-cycle-neural-network-models-keras.md)
    +   [在 Python 迷你课程中应用深度学习](docs/dl-keras/applied-deep-learning-in-python-mini-course.md)
    +   [Keras 深度学习库的二元分类教程](docs/dl-keras/binary-classification-tutorial-with-the-keras-deep-learning-library.md)
    +   [如何用 Keras 构建多层感知器神经网络模型](docs/dl-keras/build-multi-layer-perceptron-neural-network-models-keras.md)
    +   [如何在 Keras 中检查深度学习模型](docs/dl-keras/check-point-deep-learning-models-keras.md)
    +   [10 个用于 Amazon Web Services 深度学习的命令行秘籍](docs/dl-keras/command-line-recipes-deep-learning-amazon-web-services.md)
    +   [机器学习卷积神经网络的速成课程](docs/dl-keras/crash-course-convolutional-neural-networks.md)
    +   [如何在 Python 中使用 Keras 进行深度学习的度量](docs/dl-keras/custom-metrics-deep-learning-keras-python.md)
    +   [深度学习书籍](docs/dl-keras/deep-learning-books.md)
    +   [深度学习课程](docs/dl-keras/deep-learning-courses.md)
    +   [你所知道的深度学习是一种谎言](docs/dl-keras/deep-learning-for-developers.md)
    +   [如何设置 Amazon AWS EC2 GPU 以训练 Keras 深度学习模型（分步）](docs/dl-keras/develop-evaluate-large-deep-learning-models-keras-amazon-web-services.md)
    +   [神经网络中批量和迭代之间的区别是什么？](docs/dl-keras/difference-between-a-batch-and-an-epoch.md)
    +   [在 Keras 展示深度学习模型训练历史](docs/dl-keras/display-deep-learning-model-training-history-in-keras.md)
    +   [基于 Keras 的深度学习模型中的dropout正则化](docs/dl-keras/dropout-regularization-deep-learning-models-keras.md)
    +   [评估 Keras 中深度学习模型的表现](docs/dl-keras/evaluate-performance-deep-learning-models-keras.md)
    +   [如何评价深度学习模型的技巧](docs/dl-keras/evaluate-skill-deep-learning-models.md)
    +   [小批量梯度下降的简要介绍以及如何配置批量大小](docs/dl-keras/gentle-introduction-mini-batch-gradient-descent-configure-batch-size.md)
    +   [在 Keras 中获得深度学习帮助的 9 种方法](docs/dl-keras/get-help-with-keras.md)
    +   [如何使用 Keras 在 Python 中网格搜索深度学习模型的超参数](docs/dl-keras/grid-search-hyperparameters-deep-learning-models-python-keras.md)
    +   [用 Keras 在 Python 中使用卷积神经网络进行手写数字识别](docs/dl-keras/handwritten-digit-recognition-using-convolutional-neural-networks-python-keras.md)
    +   [如何用 Keras 进行预测](docs/dl-keras/how-to-make-classification-and-regression-predictions-for-deep-learning-models-in-keras.md)
    +   [用 Keras 进行深度学习的图像增强](docs/dl-keras/image-augmentation-deep-learning-keras.md)
    +   [8 个深度学习的鼓舞人心的应用](docs/dl-keras/inspirational-applications-deep-learning.md)
    +   [Python 深度学习库 Keras 简介](docs/dl-keras/introduction-python-deep-learning-library-keras.md)
    +   [Python 深度学习库 TensorFlow 简介](docs/dl-keras/introduction-python-deep-learning-library-tensorflow.md)
    +   [Python 深度学习库 Theano 简介](docs/dl-keras/introduction-python-deep-learning-library-theano.md)
    +   [如何使用 Keras 函数式 API 进行深度学习](docs/dl-keras/keras-functional-api-deep-learning.md)
    +   [Keras 深度学习库的多类分类教程](docs/dl-keras/multi-class-classification-tutorial-keras-deep-learning-library.md)
    +   [多层感知器神经网络速成课程](docs/dl-keras/neural-networks-crash-course.md)
    +   [基于卷积神经网络的 Keras 深度学习库中的目标识别](docs/dl-keras/object-recognition-convolutional-neural-networks-keras-deep-learning-library.md)
    +   [流行的深度学习库](docs/dl-keras/popular-deep-learning-libraries.md)
    +   [用深度学习预测电影评论的情感](docs/dl-keras/predict-sentiment-movie-reviews-using-deep-learning.md)
    +   [Python 中的 Keras 深度学习库的回归教程](docs/dl-keras/regression-tutorial-keras-deep-learning-library-python.md)
    +   [如何使用 Keras 获得可重现的结果](docs/dl-keras/reproducible-results-neural-networks-keras.md)
    +   [如何在 Linux 服务器上运行深度学习实验](docs/dl-keras/run-deep-learning-experiments-linux-server.md)
    +   [保存并加载您的 Keras 深度学习模型](docs/dl-keras/save-load-keras-deep-learning-models.md)
    +   [用 Keras 逐步开发 Python 中的第一个神经网络](docs/dl-keras/tutorial-first-neural-network-python-keras.md)
    +   [用 Keras 理解 Python 中的有状态 LSTM 循环神经网络](docs/dl-keras/understanding-stateful-lstm-recurrent-neural-networks-python-keras.md)
    +   [在 Python 中使用 Keras 深度学习模型和 Scikit-Learn](docs/dl-keras/use-keras-deep-learning-models-scikit-learn-python.md)
    +   [如何使用预训练的 VGG 模型对照片中的物体进行分类](docs/dl-keras/use-pre-trained-vgg-model-classify-objects-photographs.md)
    +   [在 Python 和 Keras 中对深度学习模型使用学习率调度](docs/dl-keras/using-learning-rate-schedules-deep-learning-models-python-keras.md)
    +   [如何在 Keras 中可视化深度学习神经网络模型](docs/dl-keras/visualize-deep-learning-neural-network-model-keras.md)
    +   [什么是深度学习？](docs/dl-keras/what-is-deep-learning.md)
    +   [何时使用 MLP，CNN 和 RNN 神经网络](docs/dl-keras/when-to-use-mlp-cnn-and-rnn-neural-networks.md)
    +   [为什么用随机权重初始化神经网络？](docs/dl-keras/why-initialize-a-neural-network-with-random-weights.md)
+   [Machine Learning Mastery 深度学习 NLP 教程](docs/dl-nlp/README.md)
    +   [深度学习在自然语言处理中的 7 个应用](docs/dl-nlp/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](docs/dl-nlp/beam-search-decoder-natural-language-processing.md)
    +   [深度学习文档分类的最佳实践](docs/dl-nlp/best-practices-document-classification-deep-learning.md)
    +   [关于自然语言处理的热门书籍](docs/dl-nlp/books-on-natural-language-processing.md)
    +   [在 Python 中计算文本 BLEU 分数的温和介绍](docs/dl-nlp/calculate-bleu-score-for-text-python.md)
    +   [使用编码器 - 解码器模型的用于字幕生成的注入和合并架构](docs/dl-nlp/caption-generation-inject-merge-architectures-encoder-decoder-model.md)
    +   [如何用 Python 清理机器学习的文本](docs/dl-nlp/clean-text-machine-learning-python.md)
    +   [如何配置神经机器翻译的编码器 - 解码器模型](docs/dl-nlp/configure-encoder-decoder-model-neural-machine-translation.md)
    +   [如何开始深度学习自然语言处理（7 天迷你课程）](docs/dl-nlp/crash-course-deep-learning-natural-language-processing.md)
    +   [自然语言处理的数据集](docs/dl-nlp/datasets-natural-language-processing.md)
    +   [如何开发一种深度学习的词袋模型来预测电影评论情感](docs/dl-nlp/deep-learning-bag-of-words-model-sentiment-analysis.md)
    +   [深度学习字幕生成模型的温和介绍](docs/dl-nlp/deep-learning-caption-generation-models.md)
    +   [如何在 Keras 中定义神经机器翻译的编码器 - 解码器序列 - 序列模型](docs/dl-nlp/define-encoder-decoder-sequence-sequence-model-neural-machine-translation-keras.md)
    +   [如何利用小实验在 Keras 中开发字幕生成模型](docs/dl-nlp/develop-a-caption-generation-model-in-keras.md)
    +   [如何从头开发深度学习图片标题生成器](docs/dl-nlp/develop-a-deep-learning-caption-generation-model-in-python.md)
    +   [如何在 Keras 中开发基于字符的神经语言模型](docs/dl-nlp/develop-character-based-neural-language-model-keras.md)
    +   [如何开发用于情感分析的 N-gram 多通道卷积神经网络](docs/dl-nlp/develop-n-gram-multichannel-convolutional-neural-network-sentiment-analysis.md)
    +   [如何从零开始开发神经机器翻译系统](docs/dl-nlp/develop-neural-machine-translation-system-keras.md)
    +   [如何在 Python 中用 Keras 开发基于单词的神经语言模型](docs/dl-nlp/develop-word-based-neural-language-models-python-keras.md)
    +   [如何开发一种预测电影评论情感的词嵌入模型](docs/dl-nlp/develop-word-embedding-model-predicting-movie-review-sentiment.md)
    +   [如何使用 Gensim 在 Python 中开发词嵌入](docs/dl-nlp/develop-word-embeddings-python-gensim.md)
    +   [用于文本摘要的编码器 - 解码器深度学习模型](docs/dl-nlp/encoder-decoder-deep-learning-models-text-summarization.md)
    +   [Keras 中文本摘要的编码器 - 解码器模型](docs/dl-nlp/encoder-decoder-models-text-summarization-keras.md)
    +   [用于神经机器翻译的编码器 - 解码器循环神经网络模型](docs/dl-nlp/encoder-decoder-recurrent-neural-network-models-neural-machine-translation.md)
    +   [浅谈词袋模型](docs/dl-nlp/gentle-introduction-bag-words-model.md)
    +   [文本摘要的温和介绍](docs/dl-nlp/gentle-introduction-text-summarization.md)
    +   [编码器 - 解码器循环神经网络中的注意力如何工作](docs/dl-nlp/how-does-attention-work-in-encoder-decoder-recurrent-neural-networks.md)
    +   [如何利用深度学习自动生成照片的文本描述](docs/dl-nlp/how-to-caption-photos-with-deep-learning.md)
    +   [如何开发一个单词级神经语言模型并用它来生成文本](docs/dl-nlp/how-to-develop-a-word-level-neural-language-model-in-keras.md)
    +   [浅谈神经机器翻译](docs/dl-nlp/introduction-neural-machine-translation.md)
    +   [什么是自然语言处理？](docs/dl-nlp/natural-language-processing.md)
    +   [牛津自然语言处理深度学习课程](docs/dl-nlp/oxford-course-deep-learning-natural-language-processing.md)
    +   [如何为机器翻译准备法语到英语的数据集](docs/dl-nlp/prepare-french-english-dataset-machine-translation.md)
    +   [如何为情感分析准备电影评论数据](docs/dl-nlp/prepare-movie-review-data-sentiment-analysis.md)
    +   [如何为文本摘要准备新闻文章](docs/dl-nlp/prepare-news-articles-text-summarization.md)
    +   [如何准备照片标题数据集以训练深度学习模型](docs/dl-nlp/prepare-photo-caption-dataset-training-deep-learning-model.md)
    +   [如何使用 Keras 为深度学习准备文本数据](docs/dl-nlp/prepare-text-data-deep-learning-keras.md)
    +   [如何使用 scikit-learn 为机器学习准备文本数据](docs/dl-nlp/prepare-text-data-machine-learning-scikit-learn.md)
    +   [自然语言处理神经网络模型入门](docs/dl-nlp/primer-neural-network-models-natural-language-processing.md)
    +   [对自然语言处理的深度学习的承诺](docs/dl-nlp/promise-deep-learning-natural-language-processing.md)
    +   [在 Python 中用 Keras 进行 LSTM 循环神经网络的序列分类](docs/dl-nlp/sequence-classification-lstm-recurrent-neural-networks-python-keras.md)
    +   [斯坦福自然语言处理深度学习课程评价](docs/dl-nlp/stanford-deep-learning-for-natural-language-processing-course.md)
    +   [统计语言建模和神经语言模型的简要介绍](docs/dl-nlp/statistical-language-modeling-and-neural-language-models.md)
    +   [使用 Keras 在 Python 中进行 LSTM 循环神经网络的文本生成](docs/dl-nlp/text-generation-lstm-recurrent-neural-networks-python-keras.md)
    +   [浅谈机器学习中的转换](docs/dl-nlp/transduction-in-machine-learning.md)
    +   [如何使用 Keras 将词嵌入层用于深度学习](docs/dl-nlp/use-word-embedding-layers-deep-learning-keras.md)
    +   [什么是用于文本的词嵌入](docs/dl-nlp/what-are-word-embeddings.md)
+   [Machine Learning Mastery 深度学习时间序列教程](docs/dl-ts/README.md)
    +   [如何开发人类活动识别的一维卷积神经网络模型](docs/dl-ts/cnn-models-for-human-activity-recognition-time-series-classification.md)
    +   [人类活动识别的深度学习模型](docs/dl-ts/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](docs/dl-ts/evaluate-machine-learning-algorithms-for-human-activity-recognition.md)
    +   [时间序列预测的多层感知器网络探索性配置](docs/dl-ts/exploratory-configuration-multilayer-perceptron-network-time-series-forecasting.md)
    +   [比较经典和机器学习方法进行时间序列预测的结果](docs/dl-ts/findings-comparing-classical-and-machine-learning-methods-for-time-series-forecasting.md)
    +   [如何通过深度学习快速获得时间序列预测的结果](docs/dl-ts/get-good-results-fast-deep-learning-time-series-forecasting.md)
    +   [如何利用 Python 处理序列预测问题中的缺失时间步长](docs/dl-ts/handle-missing-timesteps-sequence-prediction-problems-python.md)
    +   [如何建立预测大气污染日的概率预测模型](docs/dl-ts/how-to-develop-a-probabilistic-forecasting-model-to-predict-air-pollution-days.md)
    +   [如何开发一种熟练的机器学习时间序列预测模型](docs/dl-ts/how-to-develop-a-skilful-time-series-forecasting-model.md)
    +   [如何构建家庭用电自回归预测模型](docs/dl-ts/how-to-develop-an-autoregression-forecast-model-for-household-electricity-consumption.md)
    +   [如何开发多步空气污染时间序列预测的自回归预测模型](docs/dl-ts/how-to-develop-autoregressive-forecasting-models-for-multi-step-air-pollution-time-series-forecasting.md)
    +   [如何制定多站点多元空气污染时间序列预测的基线预测](docs/dl-ts/how-to-develop-baseline-forecasts-for-multi-site-multivariate-air-pollution-time-series-forecasting.md)
    +   [如何开发时间序列预测的卷积神经网络模型](docs/dl-ts/how-to-develop-convolutional-neural-network-models-for-time-series-forecasting.md)
    +   [如何开发卷积神经网络用于多步时间序列预测](docs/dl-ts/how-to-develop-convolutional-neural-networks-for-multi-step-time-series-forecasting.md)
    +   [如何开发单变量时间序列预测的深度学习模型](docs/dl-ts/how-to-develop-deep-learning-models-for-univariate-time-series-forecasting.md)
    +   [如何开发 LSTM 模型用于家庭用电的多步时间序列预测](docs/dl-ts/how-to-develop-lstm-models-for-multi-step-time-series-forecasting-of-household-power-consumption.md)
    +   [如何开发 LSTM 模型进行时间序列预测](docs/dl-ts/how-to-develop-lstm-models-for-time-series-forecasting.md)
    +   [如何开发多元多步空气污染时间序列预测的机器学习模型](docs/dl-ts/how-to-develop-machine-learning-models-for-multivariate-multi-step-air-pollution-time-series-forecasting.md)
    +   [如何开发多层感知器模型进行时间序列预测](docs/dl-ts/how-to-develop-multilayer-perceptron-models-for-time-series-forecasting.md)
    +   [如何开发人类活动识别时间序列分类的 RNN 模型](docs/dl-ts/how-to-develop-rnn-models-for-human-activity-recognition-time-series-classification.md)
    +   [如何开始深度学习的时间序列预测（7 天迷你课程）](docs/dl-ts/how-to-get-started-with-deep-learning-for-time-series-forecasting-7-day-mini-course.md)
    +   [如何网格搜索深度学习模型进行时间序列预测](docs/dl-ts/how-to-grid-search-deep-learning-models-for-time-series-forecasting.md)
    +   [如何对单变量时间序列预测的网格搜索朴素方法](docs/dl-ts/how-to-grid-search-naive-methods-for-univariate-time-series-forecasting.md)
    +   [如何在 Python 中搜索 SARIMA 模型超参数用于时间序列预测](docs/dl-ts/how-to-grid-search-sarima-model-hyperparameters-for-time-series-forecasting-in-python.md)
    +   [如何在 Python 中进行时间序列预测的网格搜索三次指数平滑](docs/dl-ts/how-to-grid-search-triple-exponential-smoothing-for-time-series-forecasting-in-python.md)
    +   [一个标准的人类活动识别问题的温和介绍](docs/dl-ts/how-to-load-and-explore-a-standard-human-activity-recognition-problem.md)
    +   [如何加载和探索家庭用电数据](docs/dl-ts/how-to-load-and-explore-household-electricity-usage-data.md)
    +   [如何加载，可视化和探索复杂的多变量多步时间序列预测数据集](docs/dl-ts/how-to-load-visualize-and-explore-a-complex-multivariate-multistep-time-series-forecasting-dataset.md)
    +   [如何从智能手机数据模拟人类活动](docs/dl-ts/how-to-model-human-activity-from-smartphone-data.md)
    +   [如何根据环境因素预测房间占用率](docs/dl-ts/how-to-predict-room-occupancy-based-on-environmental-factors.md)
    +   [如何使用脑波预测人眼是开放还是闭合](docs/dl-ts/how-to-predict-whether-eyes-are-open-or-closed-using-brain-waves.md)
    +   [如何在 Python 中扩展长短期内存网络的数据](docs/dl-ts/how-to-scale-data-for-long-short-term-memory-networks-in-python.md)
    +   [如何使用 TimeseriesGenerator 进行 Keras 中的时间序列预测](docs/dl-ts/how-to-use-the-timeseriesgenerator-for-time-series-forecasting-in-keras.md)
    +   [基于机器学习算法的室内运动时间序列分类](docs/dl-ts/indoor-movement-time-series-classification-with-machine-learning-algorithms.md)
    +   [用于时间序列预测的状态 LSTM 在线学习的不稳定性](docs/dl-ts/instability-online-learning-stateful-lstm-time-series-forecasting.md)
    +   [用于罕见事件时间序列预测的 LSTM 模型体系结构](docs/dl-ts/lstm-model-architecture-for-rare-event-time-series-forecasting.md)
    +   [用于时间序列预测的 4 种通用机器学习数据变换](docs/dl-ts/machine-learning-data-transforms-for-time-series-forecasting.md)
    +   [Python 中长短期记忆网络的多步时间序列预测](docs/dl-ts/multi-step-time-series-forecasting-long-short-term-memory-networks-python.md)
    +   [家庭用电机器学习的多步时间序列预测](docs/dl-ts/multi-step-time-series-forecasting-with-machine-learning-models-for-household-electricity-consumption.md)
    +   [Keras 中 LSTM 的多变量时间序列预测](docs/dl-ts/multivariate-time-series-forecasting-lstms-keras.md)
    +   [如何开发和评估朴素的家庭用电量预测方法](docs/dl-ts/naive-methods-for-forecasting-household-electricity-consumption.md)
    +   [如何为长短期记忆网络准备单变量时间序列数据](docs/dl-ts/prepare-univariate-time-series-data-long-short-term-memory-networks.md)
    +   [循环神经网络在时间序列预测中的应用](docs/dl-ts/promise-recurrent-neural-networks-time-series-forecasting.md)
    +   [如何在 Python 中使用差异变换删除趋势和季节性](docs/dl-ts/remove-trends-seasonality-difference-transform-python.md)
    +   [如何在 LSTM 中种子状态用于 Python 中的时间序列预测](docs/dl-ts/seed-state-lstms-time-series-forecasting-python.md)
    +   [使用 Python 进行时间序列预测的有状态和无状态 LSTM](docs/dl-ts/stateful-stateless-lstm-time-series-forecasting-python.md)
    +   [长短时记忆网络在时间序列预测中的适用性](docs/dl-ts/suitability-long-short-term-memory-networks-time-series-forecasting.md)
    +   [时间序列预测问题的分类](docs/dl-ts/taxonomy-of-time-series-forecasting-problems.md)
    +   [Python 中长短期记忆网络的时间序列预测](docs/dl-ts/time-series-forecasting-long-short-term-memory-network-python.md)
    +   [基于 Keras 的 Python 中 LSTM 循环神经网络的时间序列预测](docs/dl-ts/time-series-prediction-lstm-recurrent-neural-networks-python-keras.md)
    +   [Keras 中深度学习的时间序列预测](docs/dl-ts/time-series-prediction-with-deep-learning-in-python-with-keras.md)
    +   [如何用 Keras 调整 LSTM 超参数进行时间序列预测](docs/dl-ts/tune-lstm-hyperparameters-keras-time-series-forecasting.md)
    +   [如何在时间序列预测训练期间更新 LSTM 网络](docs/dl-ts/update-lstm-networks-training-time-series-forecasting.md)
    +   [如何使用 LSTM 网络的 Dropout 进行时间序列预测](docs/dl-ts/use-dropout-lstm-networks-time-series-forecasting.md)
    +   [如何使用 LSTM 网络中的特征进行时间序列预测](docs/dl-ts/use-features-lstm-networks-time-series-forecasting.md)
    +   [如何在 LSTM 网络中使用时间序列进行时间序列预测](docs/dl-ts/use-timesteps-lstm-networks-time-series-forecasting.md)
    +   [如何利用 LSTM 网络进行权重正则化进行时间序列预测](docs/dl-ts/use-weight-regularization-lstm-networks-time-series-forecasting.md)
+   [Machine Learning Mastery 线性代数教程](docs/linalg/README.md)
    +   [机器学习数学符号的基础知识](docs/linalg/basics-mathematical-notation-machine-learning.md)
    +   [用 NumPy 阵列轻松介绍广播](docs/linalg/broadcasting-with-numpy-arrays.md)
    +   [如何从 Python 中的 Scratch 计算主成分分析（PCA）](docs/linalg/calculate-principal-component-analysis-scratch-python.md)
    +   [用于编码器审查的计算线性代数](docs/linalg/computational-linear-algebra-coders-review.md)
    +   [10 机器学习中的线性代数示例](docs/linalg/examples-of-linear-algebra-in-machine-learning.md)
    +   [线性代数的温和介绍](docs/linalg/gentle-introduction-linear-algebra.md)
    +   [用 NumPy 轻松介绍 Python 中的 N 维数组](docs/linalg/gentle-introduction-n-dimensional-arrays-python-numpy.md)
    +   [机器学习向量的温和介绍](docs/linalg/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](docs/linalg/index-slice-reshape-numpy-arrays-machine-learning-python.md)
    +   [机器学习的矩阵和矩阵算法简介](docs/linalg/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](docs/linalg/introduction-to-eigendecomposition-eigenvalues-and-eigenvectors.md)
    +   [NumPy 对预期价值，方差和协方差的简要介绍](docs/linalg/introduction-to-expected-value-variance-and-covariance.md)
    +   [机器学习矩阵分解的温和介绍](docs/linalg/introduction-to-matrix-decompositions-for-machine-learning.md)
    +   [用 NumPy 轻松介绍机器学习的张量](docs/linalg/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](docs/linalg/introduction-to-types-of-matrices-in-linear-algebra.md)
    +   [用于机器学习的线性代数备忘单](docs/linalg/linear-algebra-cheat-sheet-for-machine-learning.md)
    +   [线性代数的深度学习](docs/linalg/linear-algebra-for-deep-learning.md)
    +   [用于机器学习的线性代数（7 天迷你课程）](docs/linalg/linear-algebra-machine-learning-7-day-mini-course.md)
    +   [机器学习的线性代数](docs/linalg/linear-algebra-machine-learning.md)
    +   [机器学习矩阵运算的温和介绍](docs/linalg/matrix-operations-for-machine-learning.md)
    +   [线性代数评论没有废话指南](docs/linalg/no-bullshit-guide-to-linear-algebra-review.md)
    +   [学习机器学习线性代数的主要资源](docs/linalg/resources-for-linear-algebra-in-machine-learning.md)
    +   [浅谈机器学习的奇异值分解](docs/linalg/singular-value-decomposition-for-machine-learning.md)
    +   [如何用线性代数求解线性回归](docs/linalg/solve-linear-regression-using-linear-algebra.md)
    +   [用于机器学习的稀疏矩阵的温和介绍](docs/linalg/sparse-matrices-for-machine-learning.md)
    +   [机器学习中向量规范的温和介绍](docs/linalg/vector-norms-machine-learning.md)
    +   [学习线性代数用于机器学习的 5 个理由](docs/linalg/why-learn-linear-algebra-for-machine-learning.md)
+   [Machine Learning Mastery LSTM 教程](docs/lstm/README.md)
    +   [Keras中长短期记忆模型的5步生命周期](docs/lstm/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](docs/lstm/attention-long-short-term-memory-recurrent-neural-networks.md)
    +   [CNN长短期记忆网络](docs/lstm/cnn-long-short-term-memory-networks.md)
    +   [逆向神经网络中的深度学习速成课程](docs/lstm/crash-course-recurrent-neural-networks-deep-learning.md)
    +   [可变长度输入序列的数据准备](docs/lstm/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](docs/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](docs/lstm/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](docs/lstm/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](docs/lstm/encoder-decoder-attention-sequence-to-sequence-prediction-keras.md)
    +   [编码器 - 解码器长短期存储器网络](docs/lstm/encoder-decoder-long-short-term-memory-networks.md)
    +   [神经网络中爆炸梯度的温和介绍](docs/lstm/exploding-gradients-in-neural-networks.md)
    +   [对时间反向传播的温和介绍](docs/lstm/gentle-introduction-backpropagation-time.md)
    +   [生成长短期记忆网络的温和介绍](docs/lstm/gentle-introduction-generative-long-short-term-memory-networks.md)
    +   [专家对长短期记忆网络的简要介绍](docs/lstm/gentle-introduction-long-short-term-memory-networks-experts.md)
    +   [在序列预测问题上充分利用LSTM](docs/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](docs/lstm/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](docs/lstm/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](docs/lstm/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](docs/lstm/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](docs/lstm/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](docs/lstm/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](docs/lstm/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](docs/lstm/long-short-term-memory-recurrent-neural-networks-mini-course.md)
    +   [LSTM自动编码器的温和介绍](docs/lstm/lstm-autoencoders.md)
    +   [如何用Keras中的长短期记忆模型进行预测](docs/lstm/make-predictions-long-short-term-memory-models-keras.md)
    +   [用Python中的长短期内存网络演示内存](docs/lstm/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](docs/lstm/models-sequence-prediction-recurrent-neural-networks.md)
    +   [深度学习的循环神经网络算法之旅](docs/lstm/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](docs/lstm/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](docs/lstm/return-sequences-and-return-states-for-lstms-in-keras.md)
    +   [RNN展开的温和介绍](docs/lstm/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](docs/lstm/sequence-prediction-problems-learning-lstm-recurrent-neural-networks.md)
    +   [使用序列进行预测](docs/lstm/sequence-prediction.md)
    +   [堆叠长短期内存网络](docs/lstm/stacked-long-short-term-memory-networks.md)
    +   [什么是教师强制循环神经网络？](docs/lstm/teacher-forcing-for-recurrent-neural-networks.md)
    +   [如何在Python中使用TimeDistributed Layer for Long Short-Term Memory Networks](docs/lstm/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](docs/lstm/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](docs/lstm/use-different-batch-sizes-training-predicting-python-keras.md)
+   [Machine Learning Mastery 机器学习算法教程](docs/ml-algo/README.md)
    +   [机器学习算法之旅](docs/ml-algo/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](docs/ml-algo/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](docs/ml-algo/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](docs/ml-algo/better-naive-bayes.md)
    +   [机器学习的提升和AdaBoost](docs/ml-algo/boosting-and-adaboost-for-machine-learning.md)
    +   [选择机器学习算法：Microsoft Azure的经验教训](docs/ml-algo/choosing-machine-learning-algorithms-lessons-from-microsoft-azure.md)
    +   [机器学习的分类和回归树](docs/ml-algo/classification-and-regression-trees-for-machine-learning.md)
    +   [什么是机器学习中的混淆矩阵](docs/ml-algo/confusion-matrix-machine-learning.md)
    +   [如何使用Python从头开始创建算法测试工具](docs/ml-algo/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](docs/ml-algo/create-lists-of-machine-learning-algorithms.md)
    +   [从头开始停止编码机器学习算法](docs/ml-algo/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](docs/ml-algo/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](docs/ml-algo/dont-use-random-guessing-as-your-baseline-classifier.md)
    +   [浅谈机器学习中的概念漂移](docs/ml-algo/gentle-introduction-concept-drift-machine-learning.md)
    +   [温和介绍机器学习中的偏差 - 方差权衡](docs/ml-algo/gentle-introduction-to-the-bias-variance-trade-off-in-machine-learning.md)
    +   [机器学习的梯度下降](docs/ml-algo/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）](docs/ml-algo/how-machine-learning-algorithms-work.md)
    +   [如何建立机器学习算法的直觉](docs/ml-algo/how-to-build-an-intuition-for-machine-learning-algorithms.md)
    +   [如何实现机器学习算法](docs/ml-algo/how-to-implement-a-machine-learning-algorithm.md)
    +   [如何研究机器学习算法行为](docs/ml-algo/how-to-investigate-machine-learning-algorithm-behavior.md)
    +   [如何学习机器学习算法](docs/ml-algo/how-to-learn-a-machine-learning-algorithm.md)
    +   [如何研究机器学习算法](docs/ml-algo/how-to-research-a-machine-learning-algorithm.md)
    +   [如何研究机器学习算法](docs/ml-algo/how-to-study-machine-learning-algorithms.md)
    +   [如何在Python中从头开始实现反向传播算法](docs/ml-algo/implement-backpropagation-algorithm-scratch-python.md)
    +   [如何用Python从头开始实现Bagging](docs/ml-algo/implement-bagging-scratch-python.md)
    +   [如何用Python从头开始实现基线机器学习算法](docs/ml-algo/implement-baseline-machine-learning-algorithms-scratch-python.md)
    +   [如何在Python中从头开始实现决策树算法](docs/ml-algo/implement-decision-tree-algorithm-scratch-python.md)
    +   [如何用Python从头开始实现学习向量量化](docs/ml-algo/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](docs/ml-algo/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](docs/ml-algo/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](docs/ml-algo/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](docs/ml-algo/implement-perceptron-algorithm-scratch-python.md)
    +   [如何在Python中从零开始实现随机森林](docs/ml-algo/implement-random-forest-scratch-python.md)
    +   [如何在Python中从头开始实现重采样方法](docs/ml-algo/implement-resampling-methods-scratch-python.md)
    +   [如何用Python从头开始实现简单线性回归](docs/ml-algo/implement-simple-linear-regression-scratch-python.md)
    +   [如何用Python从头开始实现堆栈泛化（Stacking）](docs/ml-algo/implementing-stacking-scratch-python.md)
    +   [K-Nearest Neighbors for Machine Learning](docs/ml-algo/k-nearest-neighbors-for-machine-learning.md)
    +   [学习机器学习的向量量化](docs/ml-algo/learning-vector-quantization-for-machine-learning.md)
    +   [机器学习的线性判别分析](docs/ml-algo/linear-discriminant-analysis-for-machine-learning.md)
    +   [机器学习的线性回归](docs/ml-algo/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](docs/ml-algo/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](docs/ml-algo/load-machine-learning-data-scratch-python.md)
    +   [机器学习的Logistic回归](docs/ml-algo/logistic-regression-for-machine-learning.md)
    +   [机器学习的Logistic回归教程](docs/ml-algo/logistic-regression-tutorial-for-machine-learning.md)
    +   [机器学习算法迷你课程](docs/ml-algo/machine-learning-algorithms-mini-course.md)
    +   [如何在Python中从头开始实现朴素贝叶斯](docs/ml-algo/naive-bayes-classifier-scratch-python.md)
    +   [朴素贝叶斯机器学习](docs/ml-algo/naive-bayes-for-machine-learning.md)
    +   [朴素贝叶斯机器学习教程](docs/ml-algo/naive-bayes-tutorial-for-machine-learning.md)
    +   [机器学习算法的过拟合和欠拟合](docs/ml-algo/overfitting-and-underfitting-with-machine-learning-algorithms.md)
    +   [参数化和非参数机器学习算法](docs/ml-algo/parametric-and-nonparametric-machine-learning-algorithms.md)
    +   [理解任何机器学习算法的6个问题](docs/ml-algo/question-to-understand-any-machine-learning-algorithm.md)
    +   [在机器学习中拥抱随机性](docs/ml-algo/randomness-in-machine-learning.md)
    +   [如何使用Python从头开始扩展机器学习数据](docs/ml-algo/scale-machine-learning-data-scratch-python.md)
    +   [机器学习的简单线性回归教程](docs/ml-algo/simple-linear-regression-tutorial-for-machine-learning.md)
    +   [有监督和无监督的机器学习算法](docs/ml-algo/supervised-and-unsupervised-machine-learning-algorithms.md)
    +   [用于机器学习的支持向量机](docs/ml-algo/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](docs/ml-algo/techniques-to-understand-machine-learning-algorithms-without-the-background-in-mathematics.md)
    +   [最好的机器学习算法](docs/ml-algo/the-best-machine-learning-algorithm.md)
    +   [教程从头开始在Python中实现k-Nearest Neighbors](docs/ml-algo/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）](docs/ml-algo/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](docs/ml-algo/use-random-forest-testing-179-classifiers-121-datasets.md)
    +   [为什么从零开始实现机器学习算法](docs/ml-algo/why-implement-a-machine-learning-algorithm-from-scratch.md)
+   [Machine Learning Mastery 机器学习入门教程](docs/ml-start/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](docs/ml-start/4-steps-to-get-started-in-machine-learning.md)
    +   [你应该培养的 5 个机器学习领域](docs/ml-start/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](docs/ml-start/a-data-driven-approach-to-machine-learning.md)
    +   [机器学习中的分析与数值解](docs/ml-start/analytical-vs-numerical-solutions-in-machine-learning.md)
    +   [应用机器学习是一种精英政治](docs/ml-start/applied-machine-learning-is-a-meritocracy.md)
    +   [机器学习的基本概念](docs/ml-start/basic-concepts-in-machine-learning.md)
    +   [如何成为数据科学家](docs/ml-start/become-data-scientist.md)
    +   [初学者如何在机器学习中弄错](docs/ml-start/beginners-get-it-wrong.md)
    +   [机器学习的最佳编程语言](docs/ml-start/best-programming-language-for-machine-learning.md)
    +   [构建机器学习组合](docs/ml-start/build-a-machine-learning-portfolio.md)
    +   [机器学习中分类与回归的区别](docs/ml-start/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](docs/ml-start/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](docs/ml-start/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](docs/ml-start/extend-machine-learning-tools.md)
    +   [通过寻找地标开始机器学习](docs/ml-start/find-machine-learning-landmarks.md)
    +   [温和地介绍预测建模](docs/ml-start/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](docs/ml-start/get-dream-job-machine-learning-delivering-results.md)
    +   [如何开始机器学习：自学蓝图](docs/ml-start/get-started-machine-learning.md)
    +   [开始并在机器学习方面取得进展](docs/ml-start/get-started-make-progress-machine-learning.md)
    +   [应用机器学习的 Hello World](docs/ml-start/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](docs/ml-start/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）](docs/ml-start/how-do-i-get-started-in-machine-learning.md)
    +   [我是如何开始机器学习的](docs/ml-start/how-i-got-started-in-machine-learning.md)
    +   [如何在机器学习中取得更好的成绩](docs/ml-start/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](docs/ml-start/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](docs/ml-start/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](docs/ml-start/investigate-machine-learning-tools.md)
    +   [获得付费申请机器学习](docs/ml-start/ladder-approach-to-becoming-a-machine-learning-consultant.md)
    +   [映射机器学习工具的景观](docs/ml-start/list-machine-learning-tools.md)
    +   [机器学习开发环境](docs/ml-start/machine-learning-development-environment.md)
    +   [机器学习金钱](docs/ml-start/machine-learning-for-money.md)
    +   [程序员的机器学习](docs/ml-start/machine-learning-for-programmers.md)
    +   [机器学习很有意思](docs/ml-start/machine-learning-is-fascinating.md)
    +   [机器学习是 Kaggle 比赛](docs/ml-start/machine-learning-is-kaggle-competitions.md)
    +   [机器学习现在很受欢迎](docs/ml-start/machine-learning-is-popular.md)
    +   [机器学习掌握方法](docs/ml-start/machine-learning-mastery-method.md)
    +   [机器学习很重要](docs/ml-start/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](docs/ml-start/machine-learning-qa-concept-drift-better-results-and-learning-faster.md)
    +   [缺乏自学机器学习的路线图](docs/ml-start/machine-learning-roadmap-your-self-study-guide-to-machine-learning.md)
    +   [机器学习很重要](docs/ml-start/machine-learning-that-matters.md)
    +   [快速了解任何机器学习工具（即使您是初学者）](docs/ml-start/machine-learning-tool-templates.md)
    +   [机器学习工具](docs/ml-start/machine-learning-tools.md)
    +   [找到你的机器学习部落](docs/ml-start/machine-learning-tribe.md)
    +   [机器学习在一年](docs/ml-start/machine-learning-year.md)
    +   [通过竞争一致的大师 Kaggle](docs/ml-start/master-kaggle-by-competing-consistently.md)
    +   [5 程序员在机器学习中开始犯错误](docs/ml-start/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）](docs/ml-start/philosophy-graduate-to-machine-learning-practitioner.md)
    +   [机器学习入门的实用建议](docs/ml-start/practical-advice-for-getting-started-in-machine-learning.md)
    +   [实用机器学习问题](docs/ml-start/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](docs/ml-start/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](docs/ml-start/proceduralize-machine-learning-tools.md)
    +   [程序员可以进入机器学习](docs/ml-start/programmers-can-get-into-machine-learning.md)
    +   [程序员应该进入机器学习](docs/ml-start/programmers-should-get-into-machine-learning.md)
    +   [项目焦点：Shashank Singh 的人脸识别](docs/ml-start/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](docs/ml-start/project-spotlight-stack-exchange-clustering-using-mahout-with-konstantin-slisenko.md)
    +   [机器学习自学指南](docs/ml-start/self-study-guide-to-machine-learning.md)
    +   [4 个自学机器学习项目](docs/ml-start/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](docs/ml-start/student-got-machine-learning-internship-job-data-science-team.md)
    +   [如何思考机器学习](docs/ml-start/think-machine-learning.md)
    +   [现实世界机器学习问题之旅](docs/ml-start/tour-of-real-world-machine-learning-problems.md)
    +   [有关机器学习的有用知识](docs/ml-start/useful-things-to-know-about-machine-learning.md)
    +   [如果我没有学位怎么办？](docs/ml-start/what-if-i-dont-have-a-degree.md)
    +   [如果我不是一个优秀的程序员怎么办？](docs/ml-start/what-if-im-not-a-good-programmer.md)
    +   [如果我不擅长数学怎么办？](docs/ml-start/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？](docs/ml-start/what-is-generalization-in-machine-learning.md)
    +   [是什么阻碍了你的机器学习目标？](docs/ml-start/what-is-holding-you-back-from-your-machine-learning-goals.md)
    +   [什么是机器学习？](docs/ml-start/what-is-machine-learning.md)
    +   [机器学习适合哪里？](docs/ml-start/where-does-machine-learning-fit-in.md)
    +   [为什么要进入机器学习？](docs/ml-start/why-get-into-machine-learning.md)
    +   [研究对您来说很重要的机器学习问题](docs/ml-start/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](docs/ml-start/youre-wrong-machine-learning-not-hard.md)
+   [Machine Learning Mastery Sklearn 教程](docs/sklearn/README.md)
    +   [Scikit-Learn 的温和介绍：Python 机器学习库](docs/sklearn/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](docs/sklearn/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](docs/sklearn/calibrated-classification-model-in-scikit-learn.md)
    +   [如何比较 Python 中的机器学习算法与 scikit-learn](docs/sklearn/compare-machine-learning-algorithms-python-scikit-learn.md)
    +   [用于机器学习开发人员的 Python 崩溃课程](docs/sklearn/crash-course-python-machine-learning-developers.md)
    +   [用 scikit-learn 在 Python 中集成机器学习算法](docs/sklearn/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](docs/sklearn/evaluate-performance-machine-learning-algorithms-python-using-resampling.md)
    +   [使用 Scikit-Learn 在 Python 中进行特征选择](docs/sklearn/feature-selection-in-python-with-scikit-learn.md)
    +   [Python 中机器学习的特征选择](docs/sklearn/feature-selection-machine-learning-python.md)
    +   [如何使用 scikit-learn 在 Python 中生成测试数据集](docs/sklearn/generate-test-datasets-python-scikit-learn.md)
    +   [scikit-learn 中的机器学习算法秘籍](docs/sklearn/get-your-hands-dirty-with-scikit-learn-now.md)
    +   [如何使用 Python 处理丢失的数据](docs/sklearn/handle-missing-data-python.md)
    +   [如何开始使用 Python 进行机器学习](docs/sklearn/how-to-get-started-with-machine-learning-in-python.md)
    +   [如何使用 Scikit-Learn 在 Python 中加载数据](docs/sklearn/how-to-load-data-in-python-with-scikit-learn.md)
    +   [Python 中概率评分方法的简要介绍](docs/sklearn/how-to-score-probability-predictions-in-python.md)
    +   [如何用 Scikit-Learn 调整算法参数](docs/sklearn/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](docs/sklearn/install-python-3-environment-mac-os-x-machine-learning-deep-learning.md)
    +   [使用 scikit-learn 进行机器学习简介](docs/sklearn/introduction-machine-learning-scikit-learn.md)
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    +   [如何使用 Python 3 为机器学习开发创建 Linux 虚拟机](docs/sklearn/linux-virtual-machine-machine-learning-development-python-3.md)
    +   [如何在 Python 中加载机器学习数据](docs/sklearn/load-machine-learning-data-python.md)
    +   [您在 Python 中的第一个机器学习项目循序渐进](docs/sklearn/machine-learning-in-python-step-by-step.md)
    +   [如何使用 scikit-learn 进行预测](docs/sklearn/make-predictions-scikit-learn.md)
    +   [用于评估 Python 中机器学习算法的度量标准](docs/sklearn/metrics-evaluate-machine-learning-algorithms-python.md)
    +   [使用 Pandas 为 Python 中的机器学习准备数据](docs/sklearn/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](docs/sklearn/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](docs/sklearn/project-spotlight-with-artem-yankov.md)
    +   [用于机器学习的 Python 生态系统](docs/sklearn/python-ecosystem-machine-learning.md)
    +   [Python 是应用机器学习的成长平台](docs/sklearn/python-growing-platform-applied-machine-learning.md)
    +   [Python 机器学习书籍](docs/sklearn/python-machine-learning-books.md)
    +   [Python 机器学习迷你课程](docs/sklearn/python-machine-learning-mini-course.md)
    +   [使用 Pandas 快速和肮脏的数据分析](docs/sklearn/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](docs/sklearn/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](docs/sklearn/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](docs/sklearn/save-load-machine-learning-models-python-scikit-learn.md)
    +   [scikit-learn Cookbook 书评](docs/sklearn/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](docs/sklearn/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](docs/sklearn/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](docs/sklearn/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](docs/sklearn/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](docs/sklearn/understand-machine-learning-data-descriptive-statistics-python.md)
    +   [使用 OpenCV，Python 和模板匹配来播放“哪里是 Waldo？”](docs/sklearn/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](docs/sklearn/visualize-machine-learning-data-python-pandas.md)
+   [Machine Learning Mastery 统计学教程](docs/stat/README.md)
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    +   [非参数统计的温和介绍](docs/stat/a-gentle-introduction-to-nonparametric-statistics.md)
    +   [Python中常态测试的温和介绍](docs/stat/a-gentle-introduction-to-normality-tests-in-python.md)
    +   [浅谈Bootstrap方法](docs/stat/a-gentle-introduction-to-the-bootstrap-method.md)
    +   [浅谈机器学习的中心极限定理](docs/stat/a-gentle-introduction-to-the-central-limit-theorem-for-machine-learning.md)
    +   [浅谈机器学习中的大数定律](docs/stat/a-gentle-introduction-to-the-law-of-large-numbers-in-machine-learning.md)
    +   [机器学习的所有统计数据](docs/stat/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](docs/stat/calculate-bootstrap-confidence-intervals-machine-learning-results-python.md)
    +   [浅谈机器学习的Chi-Squared测试](docs/stat/chi-squared-test-for-machine-learning.md)
    +   [机器学习的置信区间](docs/stat/confidence-intervals-for-machine-learning.md)
    +   [随机化在机器学习中解决混杂变量的作用](docs/stat/confounding-variables-in-machine-learning.md)
    +   [机器学习中的受控实验](docs/stat/controlled-experiments-in-machine-learning.md)
    +   [机器学习统计学速成班](docs/stat/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](docs/stat/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）](docs/stat/data-terminology-in-machine-learning.md)
    +   [Python中数据可视化方法的简要介绍](docs/stat/data-visualization-methods-in-python.md)
    +   [Python中效果大小度量的温和介绍](docs/stat/effect-size-measures-in-python.md)
    +   [估计随机机器学习算法的实验重复次数](docs/stat/estimate-number-experiment-repeats-stochastic-machine-learning-algorithms.md)
    +   [机器学习评估统计的温和介绍](docs/stat/estimation-statistics-for-machine-learning.md)
    +   [如何计算Python中的非参数秩相关性](docs/stat/how-to-calculate-nonparametric-rank-correlation-in-python.md)
    +   [如何在Python中计算数据的5位数摘要](docs/stat/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](docs/stat/how-to-code-the-students-t-test-from-scratch-in-python.md)
    +   [如何在Python中生成随机数](docs/stat/how-to-generate-random-numbers-in-python.md)
    +   [如何转换数据以更好地拟合正态分布](docs/stat/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](docs/stat/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](docs/stat/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](docs/stat/introduction-to-random-number-generators-for-machine-learning.md)
    +   [k-fold交叉验证的温和介绍](docs/stat/k-fold-cross-validation.md)
    +   [如何计算McNemar的比较两种机器学习量词的测试](docs/stat/mcnemars-test-for-machine-learning.md)
    +   [Python中非参数统计显着性测试简介](docs/stat/nonparametric-statistical-significance-tests-in-python.md)
    +   [如何在Python中使用参数统计显着性测试](docs/stat/parametric-statistical-significance-tests-in-python.md)
    +   [机器学习的预测间隔](docs/stat/prediction-intervals-for-machine-learning.md)
    +   [应用统计学与机器学习的密切关系](docs/stat/relationship-between-applied-statistics-and-machine-learning.md)
    +   [如何使用置信区间报告分类器表现](docs/stat/report-classifier-performance-confidence-intervals.md)
    +   [统计数据分布的简要介绍](docs/stat/statistical-data-distributions.md)
    +   [15 Python中的统计假设检验（备忘单）](docs/stat/statistical-hypothesis-tests-in-python-cheat-sheet.md)
    +   [统计假设检验的温和介绍](docs/stat/statistical-hypothesis-tests.md)
    +   [10如何在机器学习项目中使用统计方法的示例](docs/stat/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](docs/stat/statistical-power-and-power-analysis-in-python.md)
    +   [统计抽样和重新抽样的简要介绍](docs/stat/statistical-sampling-and-resampling.md)
    +   [比较机器学习算法的统计显着性检验](docs/stat/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](docs/stat/statistical-tolerance-intervals-in-machine-learning.md)
    +   [机器学习统计书籍](docs/stat/statistics-books-for-machine-learning.md)
    +   [评估机器学习模型的统计数据](docs/stat/statistics-for-evaluating-machine-learning-models.md)
    +   [机器学习统计（7天迷你课程）](docs/stat/statistics-for-machine-learning-mini-course.md)
    +   [用于机器学习的简明英语统计](docs/stat/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](docs/stat/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）？](docs/stat/what-is-statistics.md)
+   [Machine Learning Mastery 时间序列入门教程](docs/ts-intro/README.md)
    +   [如何在 Python 中为时间序列预测创建 ARIMA 模型](docs/ts-intro/arima-for-time-series-forecasting-with-python.md)
    +   [用 Python 进行时间序列预测的自回归模型](docs/ts-intro/autoregression-models-time-series-forecasting-python.md)
    +   [如何回溯机器学习模型的时间序列预测](docs/ts-intro/backtest-machine-learning-models-time-series-forecasting.md)
    +   [Python 中基于时间序列数据的基本特征工程](docs/ts-intro/basic-feature-engineering-time-series-data-python.md)
    +   [R 的时间序列预测热门书籍](docs/ts-intro/books-on-time-series-forecasting-with-r.md)
    +   [10 挑战机器学习时间序列预测问题](docs/ts-intro/challenge-machine-learning-time-series-forecasting-problems.md)
    +   [如何将时间序列转换为 Python 中的监督学习问题](docs/ts-intro/convert-time-series-supervised-learning-problem-python.md)
    +   [如何将时间序列数据分解为趋势和季节性](docs/ts-intro/decompose-time-series-data-trend-seasonality.md)
    +   [如何用 ARCH 和 GARCH 模拟波动率进行时间序列预测](docs/ts-intro/develop-arch-and-garch-models-for-time-series-forecasting-in-python.md)
    +   [如何将时间序列数据集与 Python 区分开来](docs/ts-intro/difference-time-series-dataset-python.md)
    +   [Python 中时间序列预测的指数平滑的温和介绍](docs/ts-intro/exponential-smoothing-for-time-series-forecasting-in-python.md)
    +   [用 Python 进行时间序列预测的特征选择](docs/ts-intro/feature-selection-time-series-forecasting-python.md)
    +   [浅谈自相关和部分自相关](docs/ts-intro/gentle-introduction-autocorrelation-partial-autocorrelation.md)
    +   [时间序列预测的 Box-Jenkins 方法简介](docs/ts-intro/gentle-introduction-box-jenkins-method-time-series-forecasting.md)
    +   [用 Python 简要介绍时间序列的时间序列预测](docs/ts-intro/gentle-introduction-random-walk-times-series-forecasting-python.md)
    +   [如何使用 Python 网格搜索 ARIMA 模型超参数](docs/ts-intro/grid-search-arima-hyperparameters-with-python.md)
    +   [如何在 Python 中加载和探索时间序列数据](docs/ts-intro/load-explore-time-series-data-python.md)
    +   [如何使用 Python 对 ARIMA 模型进行手动预测](docs/ts-intro/make-manual-predictions-arima-models-python.md)
    +   [如何用 Python 进行时间序列预测的预测](docs/ts-intro/make-predictions-time-series-forecasting-python.md)
    +   [如何使用 Python 中的 ARIMA 进行样本外预测](docs/ts-intro/make-sample-forecasts-arima-python.md)
    +   [如何利用 Python 模拟残差错误来纠正时间序列预测](docs/ts-intro/model-residual-errors-correct-time-series-forecasts-python.md)
    +   [使用 Python 进行数据准备，特征工程和时间序列预测的移动平均平滑](docs/ts-intro/moving-average-smoothing-for-time-series-forecasting-python.md)
    +   [多步时间序列预测的 4 种策略](docs/ts-intro/multi-step-time-series-forecasting.md)
    +   [如何在 Python 中规范化和标准化时间序列数据](docs/ts-intro/normalize-standardize-time-series-data-python.md)
    +   [如何利用 Python 进行时间序列预测的基线预测](docs/ts-intro/persistence-time-series-forecasting-with-python.md)
    +   [如何使用 Python 对时间序列预测数据进行功率变换](docs/ts-intro/power-transform-time-series-forecast-data-python.md)
    +   [用于时间序列预测的 Python 环境](docs/ts-intro/python-environment-time-series-forecasting.md)
    +   [如何重构时间序列预测问题](docs/ts-intro/reframe-time-series-forecasting-problem.md)
    +   [如何使用 Python 重新采样和插值您的时间序列数据](docs/ts-intro/resample-interpolate-time-series-data-python.md)
    +   [用 Python 编写 SARIMA 时间序列预测](docs/ts-intro/sarima-for-time-series-forecasting-in-python.md)
    +   [如何在 Python 中保存 ARIMA 时间序列预测模型](docs/ts-intro/save-arima-time-series-forecasting-model-python.md)
    +   [使用 Python 进行季节性持久性预测](docs/ts-intro/seasonal-persistence-forecasting-python.md)
    +   [基于 ARIMA 的 Python 历史规模敏感性预测技巧分析](docs/ts-intro/sensitivity-analysis-history-size-forecast-skill-arima-python.md)
    +   [简单的时间序列预测模型进行测试，这样你就不会欺骗自己](docs/ts-intro/simple-time-series-forecasting-models.md)
    +   [标准多变量，多步骤和多站点时间序列预测问题](docs/ts-intro/standard-multivariate-multi-step-multi-site-time-series-forecasting-problem.md)
    +   [如何使用 Python 检查时间序列数据是否是固定的](docs/ts-intro/time-series-data-stationary-python.md)
    +   [使用 Python 进行时间序列数据可视化](docs/ts-intro/time-series-data-visualization-with-python.md)
    +   [7 个机器学习的时间序列数据集](docs/ts-intro/time-series-datasets-for-machine-learning.md)
    +   [时间序列预测案例研究与 Python：波士顿每月武装抢劫案](docs/ts-intro/time-series-forecast-case-study-python-monthly-armed-robberies-boston.md)
    +   [Python 的时间序列预测案例研究：巴尔的摩的年度用水量](docs/ts-intro/time-series-forecast-study-python-annual-water-usage-baltimore.md)
    +   [使用 Python 进行时间序列预测研究：法国香槟的月销售额](docs/ts-intro/time-series-forecast-study-python-monthly-sales-french-champagne.md)
    +   [使用 Python 的置信区间理解时间序列预测不确定性](docs/ts-intro/time-series-forecast-uncertainty-using-confidence-intervals-python.md)
    +   [11 Python 中的经典时间序列预测方法（备忘单）](docs/ts-intro/time-series-forecasting-methods-in-python-cheat-sheet.md)
    +   [使用 Python 进行时间序列预测表现测量](docs/ts-intro/time-series-forecasting-performance-measures-with-python.md)
    +   [使用 Python 7 天迷你课程进行时间序列预测](docs/ts-intro/time-series-forecasting-python-mini-course.md)
    +   [时间序列预测作为监督学习](docs/ts-intro/time-series-forecasting-supervised-learning.md)
    +   [什么是时间序列预测？](docs/ts-intro/time-series-forecasting.md)
    +   [如何使用 Python 识别和删除时间序列数据的季节性](docs/ts-intro/time-series-seasonality-with-python.md)
    +   [如何在 Python 中使用和删除时间序列数据中的趋势信息](docs/ts-intro/time-series-trends-in-python.md)
    +   [如何在 Python 中调整 ARIMA 参数](docs/ts-intro/tune-arima-parameters-python.md)
    +   [如何用 Python 可视化时间序列残差预测错误](docs/ts-intro/visualize-time-series-residual-forecast-errors-with-python.md)
    +   [白噪声时间序列与 Python](docs/ts-intro/white-noise-time-series-python.md)
    +   [如何通过时间序列预测项目](docs/ts-intro/work-time-series-forecast-project.md)
+   [Machine Learning Mastery XGBoost 教程](docs/xgboost/README.md)
    +   [通过在 Python 中使用 XGBoost 提前停止来避免过度拟合](docs/xgboost/avoid-overfitting-by-early-stopping-with-xgboost-in-python.md)
    +   [如何在 Python 中调优 XGBoost 的多线程支持](docs/xgboost/best-tune-multithreading-support-xgboost-python.md)
    +   [如何配置梯度提升算法](docs/xgboost/configure-gradient-boosting-algorithm.md)
    +   [在 Python 中使用 XGBoost 进行梯度提升的数据准备](docs/xgboost/data-preparation-gradient-boosting-xgboost-python.md)
    +   [如何使用 scikit-learn 在 Python 中开发您的第一个 XGBoost 模型](docs/xgboost/develop-first-xgboost-model-python-scikit-learn.md)
    +   [如何在 Python 中使用 XGBoost 评估梯度提升模型](docs/xgboost/evaluate-gradient-boosting-models-xgboost-python.md)
    +   [在 Python 中使用 XGBoost 的特征重要性和特征选择](docs/xgboost/feature-importance-and-feature-selection-with-xgboost-in-python.md)
    +   [浅谈机器学习的梯度提升算法](docs/xgboost/gentle-introduction-gradient-boosting-algorithm-machine-learning.md)
    +   [应用机器学习的 XGBoost 简介](docs/xgboost/gentle-introduction-xgboost-applied-machine-learnin.md)
    +   [如何在 macOS 上为 Python 安装 XGBoost](docs/xgboost/install-xgboost-python-macos.md)
    +   [如何在 Python 中使用 XGBoost 保存梯度提升模型](docs/xgboost/save-gradient-boosting-models-xgboost-python.md)
    +   [从梯度提升开始，比较 165 个数据集上的 13 种算法](docs/xgboost/start-with-gradient-boosting.md)
    +   [在 Python 中使用 XGBoost 和 scikit-learn 进行随机梯度提升](docs/xgboost/stochastic-gradient-boosting-xgboost-scikit-learn-python.md)
    +   [如何使用 Amazon Web Services 在云中训练 XGBoost 模型](docs/xgboost/train-xgboost-models-cloud-amazon-web-services.md)
    +   [在 Python 中使用 XGBoost 调整梯度提升的学习率](docs/xgboost/tune-learning-rate-for-gradient-boosting-with-xgboost-in-python.md)
    +   [如何在 Python 中使用 XGBoost 调整决策树的数量和大小](docs/xgboost/tune-number-size-decision-trees-xgboost-python.md)
    +   [如何在 Python 中使用 XGBoost 可视化梯度提升决策树](docs/xgboost/visualize-gradient-boosting-decision-trees-xgboost-python.md)
    +   [在 Python 中开始使用 XGBoost 的 7 步迷你课程](docs/xgboost/xgboost-python-mini-course.md)
