Coursera Machine Learning

These are my solutions to the exercises for Stanford's Machine Learning course on Coursera: https://www.coursera.org/learn/machine-learning/home/info

On some of the exercises, I use Matlab (or Octave) as suggested by the course's teacher Andrew Ng. For some of them, I provide a solution in Python + numPy.

I think that using Jupyter Notebooks for these exercises is a great idea, they work nicely with Octave and Python. For details on how to run Jupyter Notebooks with Octave, see: https://github.com/Calysto/octave_kernel

Exercises:

1. Linear Regression: Predict profit for a food truck. Octave, Python
2. Logistic Regression: Octave
   1. Admission of students
   2. QA for micro chips
3. Multi class classification: Recognition of handwritten digits. Octave
   1. One-vs-all logistic regression
   2. Applying a pre-learned neural network
4. Training a neural network: Recognition of handwritten digits. Octave
5. Bias and Variance Problems. Octave
   1. Linear regression
   2. Polynomial features
   3. Regularization
   4. Learning curves
   5. Using cross validation data and test data