https://github.com/nex3z/machine-learning-exercise
Python implementation of the programming assignment from Machine Learning class on Coursera, which is originally implemented in Matlab/Octave.
https://github.com/nex3z/machine-learning-exercise
coursera-machine-learning machine-learning
Last synced: 4 months ago
JSON representation
Python implementation of the programming assignment from Machine Learning class on Coursera, which is originally implemented in Matlab/Octave.
- Host: GitHub
- URL: https://github.com/nex3z/machine-learning-exercise
- Owner: nex3z
- Created: 2017-01-23T14:53:25.000Z (over 9 years ago)
- Default Branch: master
- Last Pushed: 2018-10-15T16:07:38.000Z (over 7 years ago)
- Last Synced: 2025-04-26T07:35:21.623Z (about 1 year ago)
- Topics: coursera-machine-learning, machine-learning
- Language: Jupyter Notebook
- Homepage:
- Size: 65 MB
- Stars: 88
- Watchers: 7
- Forks: 68
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# machine-learning-exercise
## coursera-machine-learning-python
Python implementation of the programming assignment from [Machine Learning](https://www.coursera.org/learn/machine-learning) class on Coursera, which is originally implemented in Matlab/Octave.
- [Exercise 1-1: Linear Regression](https://github.com/nex3z/machine-learning-exercise/blob/master/coursera-machine-learning-python/ex1/ex1.ipynb)
- [Exercise 1-2: Linear Regression with Multiple Variables](https://github.com/nex3z/machine-learning-exercise/blob/master/coursera-machine-learning-python/ex1/ex1_multi.ipynb)
- [Exercise 2-1: Logistic Regression](https://github.com/nex3z/machine-learning-exercise/blob/master/coursera-machine-learning-python/ex2/ex2.ipynb)
- [Exercise 2-2: Logistic Regression with Regularization](https://github.com/nex3z/machine-learning-exercise/blob/master/coursera-machine-learning-python/ex2/ex2_reg.ipynb)
- [Exercise 3-1: One-vs-all](https://github.com/nex3z/machine-learning-exercise/blob/master/coursera-machine-learning-python/ex3/ex3.ipynb)
- [Exercise 3-2: Neural Networks](https://github.com/nex3z/machine-learning-exercise/blob/master/coursera-machine-learning-python/ex3/ex3_nn.ipynb)
- [Exercise 4: Neural Network Learning](https://github.com/nex3z/machine-learning-exercise/blob/master/coursera-machine-learning-python/ex4/ex4.ipynb)
- [Exercise 5: Regularized Linear Regression and Bias-Variance](https://github.com/nex3z/machine-learning-exercise/blob/master/coursera-machine-learning-python/ex5/ex5.ipynb)
- [Exercise 6-1: Support Vector Machines](https://github.com/nex3z/machine-learning-exercise/blob/master/coursera-machine-learning-python/ex6/ex6.ipynb)
- [Exercise 6-2: Spam Classification with SVMs](https://github.com/nex3z/machine-learning-exercise/blob/master/coursera-machine-learning-python/ex6/ex6_spam.ipynb)
- [Exercise 7-1: K-Means Clustering](https://github.com/nex3z/machine-learning-exercise/blob/master/coursera-machine-learning-python/ex7/ex7.ipynb)
- [Exercise 7-2: Principle Component Analysis](https://github.com/nex3z/machine-learning-exercise/blob/master/coursera-machine-learning-python/ex7/ex7_pca.ipynb)
- [Exercise 8-1: Anomaly Detection](https://github.com/nex3z/machine-learning-exercise/blob/master/coursera-machine-learning-python/ex8/ex8.ipynb)
- [Exercise 8-2: Collaborative Filtering](https://github.com/nex3z/machine-learning-exercise/blob/master/coursera-machine-learning-python/ex8/ex8_cofi.ipynb)