Coursera's Introduction to Machine Learning by Andrew Ng

Python Programming Assignments

This repositry contains the solutions to the python versions of the programming assignments for the Machine Learning online class taught by Professor Andrew Ng. This is perhaps the most popular introductory online machine learning class. In addition to being popular, it is also one of the best Machine learning classes any interested student can take to get started with machine learning. An unfortunate aspect of this class is that the programming assignments are in MATLAB or OCTAVE, probably because this class was made before python become the go-to language in machine learning. The Python machine learning ecosystem has grown exponentially in the past few years, and still gaining momentum.

These assignments work seamlessly with the class and do not require any of the materials published in the MATLAB assignments. The assignments use Jupyter Notebook, which provides an intuitive flow easier than the original MATLAB/OCTAVE assignments.

Downloading the Solutions to the Assignments

To get started, you can start by either downloading a zip file of these assignments by clicking on the Clone or download button. If you have git installed on your system, you can clone this repository using :

git clone https://github.com/vrn25/Machine-Learning-Coursera.git

Each assignment is contained in a separate folder. For example, assignment 1 is contained within the folder Exercise1. Each folder contains following :

• The assignment jupyter notebook, which has a .ipynb extension. All the solutions are included in it.
• Notebook checkpoints with extension .ipynb.
• A python module utils.py which contains some helper functions needed for the assignment. Functions within the utils module are called from the python notebook.
• A folder containing all the datasets required for the assignments. The file extension can be .txt or .mat according to the requirement in the notebook.
• A folder containing all the figures and graphs used in notebook.

Requirements

These assignments have been done using the following libraries:

- python==3.6.4
- numpy==1.13.3
- scipy==1.0.0
- matplotlib==2.1.2
- jupyter==1.0.0
- jupyter-client==5.0.1

Using at least these versions is recommended. Python 2 is not supported.

Python Installation

Following steps can be used to set up machine learning environment on the PC and view/edit the notebook-

It is highly recommended to use anaconda for installing python. Click here to go to Anaconda's download page. Make sure to download Python 3.6 version. If you are on a windows machine:

• Open the executable after download is complete and follow instructions.
• Once installation is complete, open Anaconda prompt from the start menu. This will open a terminal with python enabled.

If you are on a linux machine:

• Open a terminal and navigate to the directory where Anaconda was downloaded.

• Change the permission to the downloaded file so that it can be executed. So if the downloaded file name is Anaconda3-5.1.0-Linux-x86_64.sh, then use the following command:

  chmod a+x Anaconda3-5.1.0-Linux-x86_64.sh

• Now, run the installation script using ./Anaconda3-5.1.0-Linux-x86_64.sh, and follow installation instructions in the terminal.

Once you have installed python, create a new python environment will all the requirements using the following command:

conda create -n machine_learning python=3.6 scipy=1 numpy=1.13 matplotlib=2.1 jupyter

After the new environment is setup, activate it using (windows)

activate machine_learning

or if you are on a linux machine

source activate machine_learning 

Now we have our python environment all set up. The notebook can be launched from the terminal by the following command

jupyter notebook

This should automatically open a tab in the default browser. To start with assignment 1, open the notebook ./Exercise1/exercise1.ipynb.

Caveats and tips

• In many of the exercises, the regularization parameter $\lambda$ is denoted as the variable name lambda_, notice the underscore at the end of the name. This is because lambda is a reserved python keyword, and should never be used as a variable name.

• In numpy, the function dot is used to perform matrix multiplication. The operation '*' only does element-by-element multiplication (unlike MATLAB). If you are using python version 3.5+, the operator '@' is the new matrix multiplication, and it is equivalent to the dot function.