吴恩达机器学习课后作业py版.zip

# Coursera Machine Learning Assignments in Python [](https://www.linkedin.com/in/soojinro) []() []() []()  ## About If you've finished the amazing introductory Machine Learning on Coursera by Prof. Andrew Ng, you probably got familiar with Octave/Matlab programming. With this repo, you can re-implement them in Python, step-by-step, visually checking your work along the way, just as the course assignments. ## How to start ### Dependencies This project was coded in Python 3.6 * numpy * matplotlib * scipy * scikit-learn * scikit-image * nltk ### Installation The fastest and easiest way to install all these dependencies at once is to use [Anaconda](https://www.continuum.io/downloads). ## Important Note There are a couple of things to keep in mind before starting. * all column vectors from octave/matlab are flattened into a simple 1-dimensional ndarray. (e.g., y's and thetas are no longer m x 1 matrix, just a 1-d ndarray with m elements.) So in Octave/Matlab, ```matlab >> size(theta) >> (2, 1) ``` Now, it is ```python >>> theta.shape >>> (2, ) ``` * numpy.matrix is never used, just plain ol' numpy.ndarray ## Contents #### [Exercise 1](https://github.com/nsoojin/coursera-ml-py/tree/master/machine-learning-ex1) * Linear Regression * Linear Regression with multiple variables #### [Exercise 2](https://github.com/nsoojin/coursera-ml-py/tree/master/machine-learning-ex2) * Logistic Regression * Logistic Regression with Regularization #### [Exercise 3](https://github.com/nsoojin/coursera-ml-py/tree/master/machine-learning-ex3) * Multiclass Classification * Neural Networks Prediction fuction #### [Exercise 4](https://github.com/nsoojin/coursera-ml-py/tree/master/machine-learning-ex4) * Neural Networks Learning #### [Exercise 5](https://github.com/nsoojin/coursera-ml-py/tree/master/machine-learning-ex5) * Regularized Linear Regression * Bias vs. Variance #### [Exercise 6](https://github.com/nsoojin/coursera-ml-py/tree/master/machine-learning-ex6) * Support Vector Machines * Spam email Classifier #### [Exercise 7](https://github.com/nsoojin/coursera-ml-py/tree/master/machine-learning-ex7) * K-means Clustering * Principal Component Analysis #### [Exercise 8](https://github.com/nsoojin/coursera-ml-py/tree/master/machine-learning-ex8) * Anomaly Detection * Recommender Systems ## Solutions You can check out my implementation of the assignments [here](https://github.com/nsoojin/coursera-ml-py-sj). I tried to vectorize all the solutions.