+   [Machine Learning Mastery 应用机器学习教程](README.md)
+   [五个机器学习竞赛的好处](5-benefits-of-competitive-machine-learning.md)
+   [过拟合的简单直觉，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)
+   [作为搜索问题的应用机器学习的温和介绍](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？你可能过拟合了](arent-results-good-thought-youre-probably-overfitting.md)
+   [使用ROC曲线评估和比较分类器表现](assessing-comparing-classifier-performance-roc-curves-2.md)
+   [BigML评论：发现机器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做出预测](bigml-tutorial-develop-your-first-decision-tree-and-make-predictions.md)
+   [构建生产环境的机器学习基础设施](building-a-production-machine-learning-infrastructure.md)
+   [分类准确率不够：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环境中](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)
+   [探索特征工程，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)
+   [如何改进机器学习结果](how-to-improve-machine-learning-results.md)
+   [如何在机器学习竞赛中胜出](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)
+   [如何像数据科学家一样解决问题](how-to-work-through-a-problem-like-a-data-scientist.md)
+   [通过数据预处理提高模型准确率](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 Phil Brierley 的机器学习技巧](machine-learning-tips-from-a-world-class-practitioner-phil-brierley.md)
+   [模型预测准确率与机器学习中的解释](model-prediction-versus-interpretation-in-machine-learning.md)
+   [机器学习竞赛的模型选择技巧](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)
+   [应用机器学习的过程](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)
+   [通往最佳机器学习算法的简单三步法](steps-to-the-best-machine-learning-algorithm.md)
+   [对抗机器学习数据集中不平衡类别的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)
+   [黑盒机器学习的诱惑陷阱](the-seductive-trap-of-black-box-machine-learning.md)
+   [如何训练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)
+   [为什么在机器学习中单热编码数据？](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采样检查算法](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)
