https://github.com/nsoojin/coursera-ml-py
Python programming assignments for Machine Learning by Prof. Andrew Ng in Coursera
https://github.com/nsoojin/coursera-ml-py
andrew-ng-course andrew-ng-machine-learning andrew-ng-ml-course anomaly-detection coursera-machine-learning logistic-regression machine-learning-ex1 neural-network numpy-exercises principal-component-analysis python-ml support-vector-machines
Last synced: about 24 hours ago
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Python programming assignments for Machine Learning by Prof. Andrew Ng in Coursera
- Host: GitHub
- URL: https://github.com/nsoojin/coursera-ml-py
- Owner: nsoojin
- License: mit
- Created: 2017-03-21T17:58:39.000Z (about 8 years ago)
- Default Branch: master
- Last Pushed: 2020-09-02T17:38:18.000Z (over 4 years ago)
- Last Synced: 2025-05-09T21:52:34.073Z (7 days ago)
- Topics: andrew-ng-course, andrew-ng-machine-learning, andrew-ng-ml-course, anomaly-detection, coursera-machine-learning, logistic-regression, machine-learning-ex1, neural-network, numpy-exercises, principal-component-analysis, python-ml, support-vector-machines
- Language: Python
- Homepage:
- Size: 21.9 MB
- Stars: 1,413
- Watchers: 45
- Forks: 482
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- Funding: .github/FUNDING.yml
- License: LICENSE
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README
# 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.