# ML-EX-Python
These are Exercises for Coursera's MachineLearning (by Andrew Ng) by Python.
## Image of EX-example
### ex1
#### ex1_1 Linear regression with one variable
![image_ex1_1](https://github.com/X-21/ML-EX-Python/blob/master/doc/image/ex1_1.png)
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#### ex1_2 Visualizing J(θ) (Surface)
![image_ex1_2](https://github.com/X-21/ML-EX-Python/blob/master/doc/image/ex1_2.png)
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#### ex1_2 Visualizing J(θ) (Contour)
![image_ex1_3](https://github.com/X-21/ML-EX-Python/blob/master/doc/image/ex1_3.png)
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#### ex1_multi_1 Linear regression with multiple variables
Convergence of gradient descent with an appropriate learning rate
![image_ex1_multi_1](https://github.com/X-21/ML-EX-Python/blob/master/doc/image/ex1_multi_1.png)
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### ex2
#### ex2_1 Logistic Regression
![image_ex2_1](https://github.com/X-21/ML-EX-Python/blob/master/doc/image/ex2_1.png)
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#### ex2_2 Logistic Regression
Training data with decision boundary
![image_ex2_2](https://github.com/X-21/ML-EX-Python/blob/master/doc/image/ex2_2.png)
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#### ex2_LR_1 Regularized Logistic Regression
![image_ex2_LR_1](https://github.com/X-21/ML-EX-Python/blob/master/doc/image/ex2_LR_1.png)
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#### ex2_LR_2 Regularized Logistic Regression
Training data with decision boundary (λ = 1)
![image_ex2_LR_2](https://github.com/X-21/ML-EX-Python/blob/master/doc/image/ex2_LR_2.png)
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### ex3
#### ex3_1 Multi-class Classification(MNIST)
![image_ex3_1](https://github.com/X-21/ML-EX-Python/blob/master/doc/image/ex3_1.png)
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### ex5
#### ex5_1 Polynomial Regression Fit
![image_ex5_1](https://github.com/X-21/ML-EX-Python/blob/master/doc/image/ex5_1.png)
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#### ex5_2 Polynomial Regression Learning Curve
![image_ex5_2](https://github.com/X-21/ML-EX-Python/blob/master/doc/image/ex5_2.png)
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#### ex5_3 Regularization and Bias/Variance
![image_ex5_3](https://github.com/X-21/ML-EX-Python/blob/master/doc/image/ex5_3.png)
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### ex6
#### ex6_1 SVM Decision Boundary with C = 1
![image_ex6_1](https://github.com/X-21/ML-EX-Python/blob/master/doc/image/ex6_1.png)
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#### ex6_2 SVM (Gaussian Kernel) Decision Boundary (Example Dataset 2)
![image_ex6_2](https://github.com/X-21/ML-EX-Python/blob/master/doc/image/ex6_2.png)
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#### ex6_3 SVM (Gaussian Kernel) Decision Boundary (Example Dataset 3)
![image_ex6_3](https://github.com/X-21/ML-EX-Python/blob/master/doc/image/ex6_3.png)
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### ex7
#### ex7_1 K-means on example dataset
![image_ex7_1](https://github.com/X-21/ML-EX-Python/blob/master/doc/image/ex7_1.png)
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#### ex7_2 Original and reconstructed image (when using K-means to compress the image)
![image_ex7_2](https://github.com/X-21/ML-EX-Python/blob/master/doc/image/ex7_2.png)
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#### ex7_3 PCA - Computed eigenvectors of the dataset
![image_ex7_3](https://github.com/X-21/ML-EX-Python/blob/master/doc/image/ex7_3.png)
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#### ex7_4 The normalized and projected data after PCA
![image_ex7_4](https://github.com/X-21/ML-EX-Python/blob/master/doc/image/ex7_4.png)
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#### ex7_5 Original images of faces and ones reconstructed from only the top 100 principal components
![image_ex7_5](https://github.com/X-21/ML-EX-Python/blob/master/doc/image/ex7_5.png)
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#### ex7_6 PCA for visualization - 3D
![image_ex7_6](https://github.com/X-21/ML-EX-Python/blob/master/doc/image/ex7_6.png)
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#### ex7_7 2D visualization produced using PCA
![image_ex7_7](https://github.com/X-21/ML-EX-Python/blob/master/doc/image/ex7_7.png)
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### ex8
#### ex8_1 The classified anomalies
![image_ex8_1](https://github.com/X-21/ML-EX-Python/blob/master/doc/image/ex8_1.png)
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吴恩达《机器学习》课后习题 Python 版 These are Exercises for Coursera's .zip
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吴恩达《机器学习》课后习题 Python 版 These are Exercises for Coursera's .zip (144个子文件)
.gitignore 27B
ex7faces.mat 10.52MB
ex3data1.mat 7.16MB
ex4data1.mat 7.16MB
spamTrain.mat 419KB
ex8_movies.mat 218KB
ex8_movieParams.mat 196KB
spamTest.mat 110KB
ex8data2.mat 91KB
ex3weights.mat 78KB
ex4weights.mat 78KB
bird_small.mat 45KB
ex8data1.mat 9KB
ex6data2.mat 7KB
ex6data3.mat 6KB
ex7data2.mat 5KB
ex5data1.mat 1KB
ex7data1.mat 995B
ex6data1.mat 981B
README.md 4KB
Machine Learning.pdf 3.1MB
Deep Learning.pdf 2.87MB
ex7.pdf 1.04MB
ex4.pdf 576KB
ex1.pdf 478KB
ex8.pdf 443KB
ex6.pdf 328KB
ex3.pdf 287KB
ex2.pdf 228KB
ex5.pdf 183KB
ex7_5.png 337KB
ex1_2.png 152KB
ex7_6.png 118KB
ex8_1.png 82KB
ex7_2.png 79KB
ex7_7.png 78KB
ex1_3.png 53KB
ex7_1.png 44KB
ex3_1.png 38KB
bird_small.png 32KB
ex6_2.png 30KB
ex7_4.png 28KB
ex2_LR_2.png 24KB
ex5_2.png 24KB
ex5_1.png 23KB
ex2_2.png 20KB
ex1_1.png 17KB
ex1_multi_1.png 16KB
ex5_3.png 16KB
ex2_LR_1.png 16KB
ex6_3.png 15KB
ex2_1.png 15KB
ex7_3.png 11KB
ex6_1.png 9KB
ex7_pca.py 9KB
ex8_cofi.py 7KB
ex5.py 6KB
ex4.py 6KB
ex6.py 5KB
ex7.py 5KB
ex6_spam.py 4KB
ex1.py 4KB
ex8.py 4KB
Training_NN.py 3KB
ex1_multi.py 3KB
ex2.py 3KB
ex2_reg.py 3KB
nnCostFunction.py 3KB
processEmail.py 2KB
ex3_nn.py 2KB
ex3.py 2KB
checkGradients.py 2KB
checkNNGradients.py 2KB
displayData.py 2KB
displayData.py 2KB
displayData.py 2KB
fminunc_recommender.py 1KB
plotDecisionBoundary.py 1KB
runkMeans.py 1KB
cofiCostFunc.py 1KB
validationCurve.py 1015B
plotDataPoints.py 1013B
fminunc_reg.py 1011B
fminunc_lr.py 982B
plotData.py 869B
learningCurve.py 868B
selectThreshold.py 865B
multivariateGaussian.py 794B
fminunc.py 776B
mapFeature.py 705B
trainLinearReg.py 665B
findClosestCentroids.py 620B
costFunctionReg.py 606B
computeNumericalGradient.py 602B
computeNumericalGradient.py 602B
oneVsAll.py 600B
predict_nn_fp.py 575B
lrCostFunction.py 566B
linearRegCostFunction.py 566B
plotProgresskMeans.py 511B
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