ml-hello-world

Steps to a Machine Learning project

1. Define problem
2. Prepare data
3. Evaluate algorithms
4. Improve results
5. Present results

The best way to really come to terms with a new platform or tool is to work through a machine learning project end-to-end and cover the key steps. Namely, from loading data, summarizing data, evaluating algorithms and making some predictions.

Setup Python and SciPy libraries

1. Download python using Anaconda Distribution
2. Below is a list of the Python SciPy libraries required for this tutorial:
   • scipy
   • numpy
   • matplotlib
   • pandas
   • sklearn

Above libraries should be available if you setup python using Anaconda distribution.

Iris flowers classification

Problem statement

Classification of Iris flowers

Dataset

Iris Dataset
This is a good project because it is so well understood.

Attributes are numeric so you have to figure out how to load and handle data.

• It is a classification problem, allowing you to practice with perhaps an easier type of supervised learning algorithm.
• It is a multi-class classification problem (multi-nominal) that may require some specialized handling.
• It only has 4 attributes and 150 rows, meaning it is small and easily fits into memory (and a screen or A4 page).
• All of the numeric attributes are in the same units and the same scale, not requiring any special scaling or transforms to get started.