吴恩达《机器学习》课后习题 Python 版.zip

# 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  </br> </br> </br> #### ex1_2 Visualizing J(θ) (Surface)  </br> </br> </br> #### ex1_2 Visualizing J(θ) (Contour)  </br> </br> </br> #### ex1_multi_1 Linear regression with multiple variables Convergence of gradient descent with an appropriate learning rate  --- ### ex2 #### ex2_1 Logistic Regression  </br> </br> </br> #### ex2_2 Logistic Regression Training data with decision boundary  </br> </br> </br> #### ex2_LR_1 Regularized Logistic Regression  </br> </br> </br> #### ex2_LR_2 Regularized Logistic Regression Training data with decision boundary (λ = 1)  --- ### ex3 #### ex3_1 Multi-class Classification(MNIST)  --- ### ex5 #### ex5_1 Polynomial Regression Fit  </br> </br> </br> #### ex5_2 Polynomial Regression Learning Curve  </br> </br> </br> #### ex5_3 Regularization and Bias/Variance  --- ### ex6 #### ex6_1 SVM Decision Boundary with C = 1  </br> </br> </br> #### ex6_2 SVM (Gaussian Kernel) Decision Boundary (Example Dataset 2)  </br> </br> </br> #### ex6_3 SVM (Gaussian Kernel) Decision Boundary (Example Dataset 3)  --- ### ex7 #### ex7_1 K-means on example dataset  </br> </br> </br> #### ex7_2 Original and reconstructed image (when using K-means to compress the image)  </br> </br> </br> #### ex7_3 PCA - Computed eigenvectors of the dataset  </br> </br> </br> #### ex7_4 The normalized and projected data after PCA  </br> </br> </br> #### ex7_5 Original images of faces and ones reconstructed from only the top 100 principal components  </br> </br> </br> #### ex7_6 PCA for visualization - 3D  </br> </br> </br> #### ex7_7 2D visualization produced using PCA  --- ### ex8 #### ex8_1 The classified anomalies  </br>