# 机器学习Jupyter Notebook
吴恩达的Machine Learning课程是非常适合机器学习的初学者。
但其assignment使用了Matlab/Octave语言，这显然已经不适合当前机器学习/深度学习技术的发展。
此Repository将其所有课程assignment内容更新为Python版本，并采用Jupyter Notebook的形式，方便阅读和实验。
## Jupyter Notebook (Python)
* [1.linear_regression](1.linear_regression)
* [2.logistic_regression](2.logistic_regression)
* [3.multi-class_classification_and_neural_network](3.multi-class_classification_and_neural_network)
* [4.nurual_network_back_propagation](4.nurual_network_back_propagation)
* [5.bias_vs_variance](5.bias_vs_variance)
* [6.svm](6.svm)
* [7.kmeans_and_PCA](7.kmeans_and_PCA)
* [8.anomaly_detection_and_recommendation](8.anomaly_detection_and_recommendation)
## 原始MATLAB版本
* [原始Assignment的MATLAB版本](original-machine-learning-MATLAB)
## 练习的描述PDF
* [原始Assignment的文字描述](Machine_Learning_Assignment(ex1-ex8).pdf)（整合为一个PDF）
如有疑问，请Submit issue或Pull Request，谢谢
## 更多
* [深度学习的Jupyter Notebooks](https://github.com/loveunk/deep-learning-exercise-notebook)
* [机器学习、深度学习的学习路径及知识总结](https://github.com/loveunk/machine-learning-deep-learning-notes)
Andrew Ng机器学习对应Python Jupyter Notebook.zip （171个子文件） 
ex7.K-means_and_PCA.ipynb 1MB ex6.SVM.ipynb 343KB ex5.bias_vs_variance.ipynb 137KB ex4.nerual_network_backpropagation.ipynb 125KB ex8.Anomaly_Detection_and_Recommendation.ipynb 96KB sample_for_scipy.optimize.minimize.ipynb 78KB ex2.logistic_regression.ipynb 34KB ex1.linear_regreesion.ipynb 20KB ex3.Multi-class_Classification.ipynb 15KB ex3.neural_network.ipynb 10KB porterStemmer.m 10KB fmincg.m 9KB fmincg.m 9KB fmincg.m 9KB fmincg.m 9KB ex4.m 8KB ex7_pca.m 7KB ex8_cofi.m 7KB ex5.m 7KB svmTrain.m 6KB ex7.m 6KB ex6_spam.m 4KB ex1_multi.m 4KB ex2.m 4KB ex6.m 4KB processEmail.m 4KB ex8.m 4KB ex1.m 4KB ex2_reg.m 4KB nnCostFunction.m 4KB learningCurve.m 3KB ex3_nn.m 3KB ex3.m 3KB cofiCostFunc.m 2KB oneVsAll.m 2KB validationCurve.m 2KB emailFeatures.m 2KB runkMeans.m 2KB lrCostFunction.m 2KB checkNNGradients.m 2KB dataset3Params.m 2KB svmPredict.m 2KB checkCostFunction.m 2KB predictOneVsAll.m 1KB displayData.m 1KB displayData.m 1KB displayData.m 1KB plotDecisionBoundary.m 1KB selectThreshold.m 1KB featureNormalize.m 1KB costFunctionReg.m 1KB computeCentroids.m 1KB predict.m 1KB linearRegCostFunction.m 1KB findClosestCentroids.m 1KB computeNumericalGradient.m 1KB computeNumericalGradient.m 1KB costFunction.m 1KB recoverData.m 1006B plotData.m 983B gradientDescentMulti.m 982B randInitializeWeights.m 979B gradientDescent.m 957B estimateGaussian.m 943B projectData.m 940B pca.m 864B debugInitializeWeights.m 841B plotProgresskMeans.m 840B multivariateGaussian.m 808B plotFit.m 804B plotData.m 791B getVocabList.m 761B predict.m 756B computeCostMulti.m 743B gaussianKernel.m 739B visualizeBoundary.m 729B trainLinearReg.m 714B computeCost.m 703B polyFeatures.m 703B sigmoidGradient.m 698B normalEqn.m 675B kMeansInitCentroids.m 660B loadMovieList.m 651B predict.m 585B visualizeFit.m 582B plotData.m 569B warmUpExercise.m 519B featureNormalize.m 510B featureNormalize.m 510B mapFeature.m 508B normalizeRatings.m 479B sigmoid.m 451B plotDataPoints.m 434B visualizeBoundaryLinear.m 410B readFile.m 396B linearKernel.m 323B drawLine.m 232B sigmoid.m 137B sigmoid.m 137B ex7faces.mat 10.52MB共 171 条
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