【机器学习】全教程-吴恩达-FlyAI

【机器学习】全教程-吴恩达

机器学习

最后更新 2021-01-07 11:02 阅读 72

最后更新 2021-01-07 11:02

阅读 72

机器学习

# 机器学习-吴恩达
    教程内容收集整理自网络，如有侵权，请联系删除！

![](https://dataset.flyai.com/机器学习.jpg)
# 目录
## 章节1 初识机器学习
* [01欢迎参加《机器学习》课程](https://static.flyai.com/video/machine-learning-Andew-Ng/01欢迎参加《机器学习》课程.mp4)
* [02什么是机器学习](https://static.flyai.com/video/machine-learning-Andew-Ng/02什么是机器学习.mp4)
* [03监督学习](https://static.flyai.com/video/machine-learning-Andew-Ng/03监督学习.mp4)
* [04无监督学习](https://static.flyai.com/video/machine-learning-Andew-Ng/04无监督学习.mp4)
## 章节2 单变量线性回归
* [06模型描述](https://static.flyai.com/video/machine-learning-Andew-Ng/06模型描述.mp4)
* [07代价函数](https://static.flyai.com/video/machine-learning-Andew-Ng/07代价函数.mp4)
* [08代价函数（一）](https://static.flyai.com/video/machine-learning-Andew-Ng/08代价函数（一）.mp4)
* [09代价函数（二）](https://static.flyai.com/video/machine-learning-Andew-Ng/09代价函数（二）.mp4)
* [10梯度下降](https://static.flyai.com/video/machine-learning-Andew-Ng/10梯度下降.mp4)
* [11梯度下降知识点总结](https://static.flyai.com/video/machine-learning-Andew-Ng/11梯度下降知识点总结.mp4)
* [12线性回归的梯度下降](https://static.flyai.com/video/machine-learning-Andew-Ng/12线性回归的梯度下降.mp4)
## 章节3 线性回归回顾
* [14矩阵和向量](https://static.flyai.com/video/machine-learning-Andew-Ng/14矩阵和向量.mp4)
* [15加法和标量乘法](https://static.flyai.com/video/machine-learning-Andew-Ng/15加法和标量乘法.mp4)
* [16矩阵向量乘法](https://static.flyai.com/video/machine-learning-Andew-Ng/16矩阵向量乘法.mp4)
* [17矩阵乘法](https://static.flyai.com/video/machine-learning-Andew-Ng/17矩阵乘法.mp4)
* [18矩阵乘法特征](https://static.flyai.com/video/machine-learning-Andew-Ng/18矩阵乘法特征.mp4)
* [19逆和转置](https://static.flyai.com/video/machine-learning-Andew-Ng/19逆和转置.mp4)
## 章节4 配置
* [20-26配置](tool_configure.md)
## 章节5 多变量线性回归
* [27多功能](https://static.flyai.com/video/machine-learning-Andew-Ng/27多功能.mp4)
* [28多元梯度下降法](https://static.flyai.com/video/machine-learning-Andew-Ng/28多元梯度下降法.mp4)
* [29多元梯度下降法演练I-特征缩放](https://static.flyai.com/video/machine-learning-Andew-Ng/29多元梯度下降法演练I-特征缩放.mp4)
* [30多元梯度下降法II-学习率](https://static.flyai.com/video/machine-learning-Andew-Ng/30多元梯度下降法II-学习率.mp4)
* [31特征和多项式回归](https://static.flyai.com/video/machine-learning-Andew-Ng/31特征和多项式回归.mp4)
* [32正规方程（区别于迭代方法的直接解法）](https://static.flyai.com/video/machine-learning-Andew-Ng/32正规方程（区别于迭代方法的直接解法）.mp4)
* [33正规方程在矩阵不可逆情况下的解决方法](https://static.flyai.com/video/machine-learning-Andew-Ng/33正规方程在矩阵不可逆情况下的解决方法.mp4)
* [34完成并提交编程作业](https://static.flyai.com/video/machine-learning-Andew-Ng/34完成并提交编程作业.mp4)
## 章节6 Octave Matlab教程
* [35基本操作](https://static.flyai.com/video/machine-learning-Andew-Ng/35基本操作.mp4)
* [36移动数据](https://static.flyai.com/video/machine-learning-Andew-Ng/36移动数据.mp4)
* [37计算数据](https://static.flyai.com/video/machine-learning-Andew-Ng/37计算数据.mp4)
* [38数据绘制](https://static.flyai.com/video/machine-learning-Andew-Ng/38数据绘制.mp4)
* [39控制语句：for,while,if语句](https://static.flyai.com/video/machine-learning-Andew-Ng/39控制语句：for,while,if语句.mp4)
* [40矢量](https://static.flyai.com/video/machine-learning-Andew-Ng/40矢量.mp4)
## 章节7 Logistic回归
* [42分类](https://static.flyai.com/video/machine-learning-Andew-Ng/42分类.mp4)
* [43假设陈述](https://static.flyai.com/video/machine-learning-Andew-Ng/43假设陈述.mp4)
* [44决策界限](https://static.flyai.com/video/machine-learning-Andew-Ng/44决策界限.mp4)
* [45代价函数](https://static.flyai.com/video/machine-learning-Andew-Ng/45代价函数.mp4)
* [46简化代价函数与梯度下降](https://static.flyai.com/video/machine-learning-Andew-Ng/46简化代价函数与梯度下降.mp4)
* [47高级优化](https://static.flyai.com/video/machine-learning-Andew-Ng/47高级优化.mp4)
* [48多元分类：一对多](https://static.flyai.com/video/machine-learning-Andew-Ng/48多元分类：一对多.mp4)
## 章节8 正则化
* [50过拟合问题](https://static.flyai.com/video/machine-learning-Andew-Ng/50过拟合问题.mp4)
* [51代价函数](https://static.flyai.com/video/machine-learning-Andew-Ng/51代价函数.mp4)
* [52线性回归的正则化](https://static.flyai.com/video/machine-learning-Andew-Ng/52线性回归的正则化.mp4)
* [53Logistic回归的正则化](https://static.flyai.com/video/machine-learning-Andew-Ng/53Logistic回归的正则化.mp4)
## 章节9 神经网络学习
* [54非线性假设](https://static.flyai.com/video/machine-learning-Andew-Ng/54非线性假设.mp4)
* [55神经元与大脑](https://static.flyai.com/video/machine-learning-Andew-Ng/55神经元与大脑.mp4)
* [56模型展示I](https://static.flyai.com/video/machine-learning-Andew-Ng/56模型展示I.mp4)
* [57模型展示II](https://static.flyai.com/video/machine-learning-Andew-Ng/57模型展示II.mp4)
* [58例子与直觉理解I](https://static.flyai.com/video/machine-learning-Andew-Ng/58例子与直觉理解I.mp4)
* [59例子与直觉理解II](https://static.flyai.com/video/machine-learning-Andew-Ng/59例子与直觉理解II.mp4)
* [60多元分类](https://static.flyai.com/video/machine-learning-Andew-Ng/60多元分类.mp4)
## 章节10 神经网络参数的反向传播算法
* [61代价函数](https://static.flyai.com/video/machine-learning-Andew-Ng/61代价函数.mp4)
* [62反向传播算法](https://static.flyai.com/video/machine-learning-Andew-Ng/62反向传播算法.mp4)
* [63理解反向传播](https://static.flyai.com/video/machine-learning-Andew-Ng/63理解反向传播.mp4)
* [64使用注意：展开函数](https://static.flyai.com/video/machine-learning-Andew-Ng/64使用注意：展开函数.mp4)
* [65梯度检测](https://static.flyai.com/video/machine-learning-Andew-Ng/65梯度检测.mp4)
* [66随机初始化](https://static.flyai.com/video/machine-learning-Andew-Ng/66随机初始化.mp4)
* [67组合到一起](https://static.flyai.com/video/machine-learning-Andew-Ng/67组合到一起.mp4)
* [68无人驾驶](https://static.flyai.com/video/machine-learning-Andew-Ng/68无人驾驶.mp4)
## 章节11 应用机器学习的建议
* [69决定下一步做什么](https://static.flyai.com/video/machine-learning-Andew-Ng/69决定下一步做什么.mp4)
* [70评估假设](https://static.flyai.com/video/machine-learning-Andew-Ng/70评估假设.mp4)
* [71模型选择和训练、验证、测试集](https://static.flyai.com/video/machine-learning-Andew-Ng/71模型选择和训练、验证、测试集.mp4)
* [72诊断偏差与方差](https://static.flyai.com/video/machine-learning-Andew-Ng/72诊断偏差与方差.mp4)
* [73正则化和偏差、方程](https://static.flyai.com/video/machine-learning-Andew-Ng/73正则化和偏差、方程.mp4)
* [74学习曲线](https://static.flyai.com/video/machine-learning-Andew-Ng/74学习曲线.mp4)
## 章节12 机器学习系统设计
* [75决定接下来做什么](https://static.flyai.com/video/machine-learning-Andew-Ng/75决定接下来做什么.mp4)
* [76确定执行的优先级](https://static.flyai.com/video/machine-learning-Andew-Ng/76确定执行的优先级.mp4)
* [77误差分析](https://static.flyai.com/video/machine-learning-Andew-Ng/77误差分析.mp4)
* [78不对称性分类的误差评估](https://static.flyai.com/video/machine-learning-Andew-Ng/78不对称性分类的误差评估.mp4)
* [79精确度和召回率的权衡](https://static.flyai.com/video/machine-learning-Andew-Ng/79精确度和召回率的权衡.mp4)
* [80机器学习数据](https://static.flyai.com/video/machine-learning-Andew-Ng/80机器学习数据.mp4)
## 章节13 支持向量机
* [81优化目标](https://static.flyai.com/video/machine-learning-Andew-Ng/81优化目标.mp4)
* [82直观上对大间隔的理解](https://static.flyai.com/video/machine-learning-Andew-Ng/82直观上对大间隔的理解.mp4)
* [83大间隔分类器的数学原理](https://static.flyai.com/video/machine-learning-Andew-Ng/83大间隔分类器的数学原理.mp4)
* [84核函数](https://static.flyai.com/video/machine-learning-Andew-Ng/84核函数.mp4)
* [85核函数2](https://static.flyai.com/video/machine-learning-Andew-Ng/85核函数2.mp4)
* [86使用SVM](https://static.flyai.com/video/machine-learning-Andew-Ng/86使用SVM.mp4)
## 章节14 无监督学习
* [87无监督学习](https://static.flyai.com/video/machine-learning-Andew-Ng/87无监督学习.mp4)
* [88K-Means算法](https://static.flyai.com/video/machine-learning-Andew-Ng/88K-Means算法.mp4)
* [89优化目标](https://static.flyai.com/video/machine-learning-Andew-Ng/89优化目标.mp4)
* [90随机初始化](https://static.flyai.com/video/machine-learning-Andew-Ng/90随机初始化.mp4)
* [91选取聚类数量](https://static.flyai.com/video/machine-learning-Andew-Ng/91选取聚类数量.mp4)
## 章节15 降维
* [92目标一 数据压缩](https://static.flyai.com/video/machine-learning-Andew-Ng/92目标一数据压缩.mp4)
* [93目标二 可视化](https://static.flyai.com/video/machine-learning-Andew-Ng/93目标二可视化.mp4)
* [94主成分分析问题规划1](https://static.flyai.com/video/machine-learning-Andew-Ng/94主成分分析问题规划1.mp4)
* [95主成分分析问题规划2](https://static.flyai.com/video/machine-learning-Andew-Ng/95主成分分析问题规划2.mp4)
* [96压缩重现](https://static.flyai.com/video/machine-learning-Andew-Ng/96压缩重现.mp4)
* [97主成分数量选择](https://static.flyai.com/video/machine-learning-Andew-Ng/97主成分数量选择.mp4)
* [98应用PCA的建议](https://static.flyai.com/video/machine-learning-Andew-Ng/98应用PCA的建议.mp4)
## 章节16 异常检测
* [99问题动机](https://static.flyai.com/video/machine-learning-Andew-Ng/99问题动机.mp4)
* [100高斯分布](https://static.flyai.com/video/machine-learning-Andew-Ng/100高斯分布.mp4)
* [101算法](https://static.flyai.com/video/machine-learning-Andew-Ng/101算法.mp4)
* [102开发和评估异常检测系统](https://static.flyai.com/video/machine-learning-Andew-Ng/102开发和评估异常检测系统.mp4)
* [103异常检测VS监督学习](https://static.flyai.com/video/machine-learning-Andew-Ng/103异常检测VS监督学习.mp4)
* [104选择要使用的功能](https://static.flyai.com/video/machine-learning-Andew-Ng/104选择要使用的功能.mp4)
* [105多变量高斯分布](https://static.flyai.com/video/machine-learning-Andew-Ng/105多变量高斯分布.mp4)
* [106使用多变量高斯分布的异常检测](https://static.flyai.com/video/machine-learning-Andew-Ng/106使用多变量高斯分布的异常检测.mp4)
## 章节17 推荐系统
* [107问题规划](https://static.flyai.com/video/machine-learning-Andew-Ng/107问题规划.mp4)
* [108基于内容的推荐算法](https://static.flyai.com/video/machine-learning-Andew-Ng/108基于内容的推荐算法.mp4)
* [109协同过滤](https://static.flyai.com/video/machine-learning-Andew-Ng/109协同过滤.mp4)
* [110协同过滤算法](https://static.flyai.com/video/machine-learning-Andew-Ng/110协同过滤算法.mp4)
* [111矢量化：低轶矩阵分解](https://static.flyai.com/video/machine-learning-Andew-Ng/111矢量化：低轶矩阵分解.mp4)
* [112实施细节：均值规范化](https://static.flyai.com/video/machine-learning-Andew-Ng/112实施细节：均值规范化.mp4)
## 章节18 大规模机器学习
* [113学习大数据集](https://static.flyai.com/video/machine-learning-Andew-Ng/113学习大数据集.mp4)
* [114随机梯度下降](https://static.flyai.com/video/machine-learning-Andew-Ng/114随机梯度下降.mp4)
* [115Mini-Batch 梯度下降](https://static.flyai.com/video/machine-learning-Andew-Ng/115Mini-Batch梯度下降.mp4)
* [116随机梯度下降收敛](https://static.flyai.com/video/machine-learning-Andew-Ng/116随机梯度下降收敛.mp4)
* [117在线学习](https://static.flyai.com/video/machine-learning-Andew-Ng/117在线学习.mp4)
* [118减少映射与数据并行](https://static.flyai.com/video/machine-learning-Andew-Ng/118减少映射与数据并行.mp4)
## 章节19 应用举例：照片OCR
* [119问题描述与OCR pipeline](https://static.flyai.com/video/machine-learning-Andew-Ng/119问题描述与OCR-pipeline.mp4)
* [120滑动窗口](https://static.flyai.com/video/machine-learning-Andew-Ng/120滑动窗口.mp4)
* [121获取大量数据和人工数据](https://static.flyai.com/video/machine-learning-Andew-Ng/121获取大量数据和人工数据.mp4)
* [122天花板分析：下一步工作的pipeline](https://static.flyai.com/video/machine-learning-Andew-Ng/122天花板分析：下一步工作的pipeline.mp4)
## 章节20 总结与感谢
* [123总结与感谢](https://static.flyai.com/video/machine-learning-Andew-Ng/123总结与感谢.mp4)

本作品采用知识共享署名-非商业性使用-相同方式共享 4.0 国际许可协议进行许可，转载请附上原文出处链接和本声明。
本文链接地址：https://www.flyai.com/article/800

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