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.
This project was coded in Python 3.6
- numpy
- matplotlib
- scipy
- scikit-learn
- scikit-image
- nltk
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.