nsoojin/coursera-ml-py
Python programming assignments for Machine Learning by Prof. Andrew Ng in Coursera
| repo name | nsoojin/coursera-ml-py |
| repo link | https://github.com/nsoojin/coursera-ml-py |
| homepage | |
| language | Python |
| size (curr.) | 22983 kB |
| stars (curr.) | 837 |
| created | 2017-03-21 |
| license | MIT License |
Coursera Machine Learning Assignments in Python
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.
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,
Now, it is>> size(theta) >> (2, 1)>>> theta.shape >>> (2, ) - numpy.matrix is never used, just plain ol' numpy.ndarray
Contents
Exercise 1
- Linear Regression
- Linear Regression with multiple variables
Exercise 2
- Logistic Regression
- Logistic Regression with Regularization
Exercise 3
- Multiclass Classification
- Neural Networks Prediction fuction
Exercise 4
- Neural Networks Learning
Exercise 5
- Regularized Linear Regression
- Bias vs. Variance
Exercise 6
- Support Vector Machines
- Spam email Classifier
Exercise 7
- K-means Clustering
- Principal Component Analysis
Exercise 8
- Anomaly Detection
- Recommender Systems
Solutions
You can check out my implementation of the assignments here. I tried to vectorize all the solutions.
