Joe McCarthy,
*Director, Analytics & Data Science*, Atigeo, LLC

In [1]:

```
from IPython.display import display, Image, HTML
```

There are a variety of Python libraries - e.g., Scikit-Learn - for building more full-featured decision trees and other types of models based on a variety of machine learning algorithms. Hopefully, this primer will have prepared you for learning how to use those libraries effectively.

Many Python-based machine learning libraries use other external Python libraries such as NumPy, SciPy, Matplotlib and pandas. There are tutorials available for each of these libraries, including the following:

- Tentative NumPy Tutorial
- SciPy Tutorial
- Matplotlib PyPlot Tutorial
- Pandas Tutorials (especially 10 Minutes to Pandas)

There are many machine learning or data science resources that may be useful to help you continue the journey. Here is a sampling:

- Scikit-learn's tutorial, An introduction to machine learning with scikit-learn
- Kevin Markham's video series (on the Kaggle blog), An introduction to machine learning with scikit-learn
- Kaggle's Getting Started With Python For Data Science
- Coursera's Introduction to Data Science
- Olivier Grisel's Strata 2014 tutorial, Parallel Machine Learning with scikit-learn and IPython

Please feel free to contact the author (Joe McCarthy) to suggest additional resources.

Notebooks in this primer: