This repository contains solutions to the exercises in the course Machine Learning. The course is dictated online on Coursera by Dr Andrew Ng from Stanford's university. They are originally proposed to be solved using Octave. Regardless, I decided to solve them with python, with some little extra work.
At the present time, I have uploaded the solutions of exercises 1 to 5. T he remaining three assignments will be uploaded soon, once they have been elegantly formatted. A pdf with each exercises description can be found inside the relevant folder in the repository. The list of problems in the course is as follows:
- Linear regression (univariate and multivariate)
- Logistic regression (binary classification)
- Logistic regression (multiclass classification) and Neural Networks
- Neural Networks (backpropagation, prediction and accuracy)
- Understanding bias and variance
- Support Vector Machines (yet to upload)
- K-means Clustering and Principal Component Analysis (yet to upload)
- Anomaly Detection and Recommender Systems (yet to upload)