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