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