BulletDataSample

The scikit-learn flow chart.

Many of the following tutorials and exercises will be driven by the iPython (Jupyter) Notebook, which is an interactive environment for executing Python. These iPython notebooks can optionally be viewed online or downloaded and interacted with locally on your own computer.

▪  iPython Notebook Overview from Stanford

Also note that the tutorials below are from a number of online sources. All Notebooks have been attributed to the authors; if, for some reason, you find that someone has not been properly credited for their work, please let me know and the situation will be rectified ASAP. In particular, I would like to tip my hat to Jake VanderPlas, Randal Olson, Donne Martin, Kevin Markham, and Colin Raffel for their fantastic freely-available resources.

Our first tutorials for getting our feet wet with scikit-learn follow. I suggest doing all of these in order before moving to the following steps.

A general introduction to scikit-learn, Python's most-used general purpose machine learning library, covering the k-nearest neighbors algorithm:

▪  An Introduction to scikit-learn by Jake VanderPlas

A more in-depth and expanded introduction, including a starter project with a well-known dataset from start to finish:

▪  Example Machine Learning Notebook by Randal Olson

A focus on strategies for evaluating different models in scikit-learn, covering train/test dataset splits:

▪  Model Evaluation by Kevin Markham

Step 5: Machine Learning Topics with Python

With a foundation having been laid in scikit-learn, we can move on to some more in-depth explorations of the various common, and useful, algorithms. We start with k-means clustering, one of the most well-known machine learning algorithms. It is a simple and often effective method for solving unsupervised learning problems:

▪  k-means Clustering by Jake VanderPlas

Next, we move back toward classification, and take a look at one of the most historically popular classification methods:

▪  Decision Trees via The Grimm Scientist

From classification, we look at continuous numeric prediction:

▪  Linear Regression by Jake VanderPlas

We can then leverage regression for classification problems, via logistic regression:

▪  Logistic Regression by Kevin Markham