Coursera Machine Learning Assignments in Python
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
This project was coded in Python 3.6
The fastest and easiest way to install all these dependencies at once is to use Anaconda.
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
- Linear Regression
- Linear Regression with multiple variables
- Logistic Regression
- Logistic Regression with Regularization
- Multiclass Classification
- Neural Networks Prediction fuction
- Neural Networks Learning
- Regularized Linear Regression
- Bias vs. Variance
- Support Vector Machines
- Spam email Classifier
- K-means Clustering
- Principal Component Analysis
- Anomaly Detection
- Recommender Systems
You can check out my implementation of the assignments here. I tried to vectorize all the solutions.