This tutorial will teach you the basics of machine learning. It will give you a brief overview of the basic concepts of how to build powerful predictive models from labeled data.

If you have any questions about the tutorial, please don't hesitate to contact me. You can either open an "issue" on GitHub or reach me via email at [email protected].

• View the code notebook: tutorial.ipynb

If you already have a GitHub account, the probably most convenient way to obtain the tutorial material is to clone this GitHub repository via git clone https://github.com/billy-odera/Machine-Learning and fetch updates via pull origin master

If you don’t have an GitHub account, you can download the repository as a .zip file by heading over to the GitHub repository (https://github.com/billy-odera/Machine-Learning) in your browser and click the green “Download” button in the upper right.

The tutorial code should be compatible to both Python 2.7 and Python 3.x. but will require recent installations of

• NumPy
• SciPy
• matplotlib
• pandas
• scikit-learn
• IPython
• Jupyter Notebook
• watermark

To install these libraries it's most convenient to either use conda or pip, e.g.,

conda install numpy

or

pip install numpy

etc.