Implementing machine learning algorithms with Python
After taking Andrew Ng's machine learning course through Coursera, I've decided to try implementing some of the learning algorithms using python. The course focuses on using GNU Octave / MATLAB as these languages are built to make matrix operations a breeze. I think implmenting learning algorithms in python will be good practice to help the concepts sink in. I'll be writing up each algorithm along with the associated theory and an example in Jupyter notebooks in my ml-python repo.
As of wrting this post, I've already implemented linear regression and logistic regression but have a few more in the works, namely k-means clustering and artificial neural networks. Stay tuned and I'll update this post as I add them.
Current Implementations:
- Linear Regression
- Logistic Regression
- K-Means (Added Sep 9, 2015)
- Artificial Neural Networks (Added Sep 24, 2015)
- Support Vector Machines (Added Feb 9, 2016)
Upcoming Implementations / Notebooks:
- Limitations of k-means
Support vector machines- Cross-validation