Jupyter Snippet PDSH 05.00-Machine-Learning

This notebook contains an excerpt from the Python Data Science Handbook by Jake VanderPlas; the content is available on GitHub.

The text is released under the CC-BY-NC-ND license, and code is released under the MIT license. If you find this content useful, please consider supporting the work by buying the book!

< Further Resources | Contents | What Is Machine Learning? >

Machine Learning

In many ways, machine learning is the primary means by which data science manifests itself to the broader world. Machine learning is where these computational and algorithmic skills of data science meet the statistical thinking of data science, and the result is a collection of approaches to inference and data exploration that are not about effective theory so much as effective computation.

The term “machine learning” is sometimes thrown around as if it is some kind of magic pill: apply machine learning to your data, and all your problems will be solved! As you might expect, the reality is rarely this simple. While these methods can be incredibly powerful, to be effective they must be approached with a firm grasp of the strengths and weaknesses of each method, as well as a grasp of general concepts such as bias and variance, overfitting and underfitting, and more.

This chapter will dive into practical aspects of machine learning, primarily using Python’s Scikit-Learn package. This is not meant to be a comprehensive introduction to the field of machine learning; that is a large subject and necessitates a more technical approach than we take here. Nor is it meant to be a comprehensive manual for the use of the Scikit-Learn package (for this, you can refer to the resources listed in Further Machine Learning Resources). Rather, the goals of this chapter are:

To introduce the fundamental vocabulary and concepts of machine learning.
To introduce the Scikit-Learn API and show some examples of its use.
To take a deeper dive into the details of several of the most important machine learning approaches, and develop an intuition into how they work and when and where they are applicable.

Much of this material is drawn from the Scikit-Learn tutorials and workshops I have given on several occasions at PyCon, SciPy, PyData, and other conferences. Any clarity in the following pages is likely due to the many workshop participants and co-instructors who have given me valuable feedback on this material over the years!

Finally, if you are seeking a more comprehensive or technical treatment of any of these subjects, I’ve listed several resources and references in Further Machine Learning Resources.

< Further Resources | Contents | What Is Machine Learning? >