Machine Learning for Civil and Environmental Engineers

6 Coding-free and Coding-based Approaches to Machine Learning

You take the blue pill, the story ends, you wake up in your bed and believe whatever you want to believe. You take the red pill, you stay in wonderland, and I show you how deep the rabbit hole goes.

Morpheus (The Matrix, 1999)

Synopsis

A few chapters ago, I took the liberty of outlining my thoughts on why a good component of the inertia against ML lies in our lack of coding/programming in our curriculum.¹ It felt more beneficial to find some solutions that do not require us to change our domain abruptly. The simplest of all solutions was to adopt a coding-free approach to ML where engineers with limited knowledge/resources of programming can still leverage the full potential of ML without the need to resort to traditional coding. Thus, this chapter offers a look into the two main approaches to practicing ML, coding-free and coding-based. In this journey, we will go over a few platforms that I found most suitable for engineers, and especially students, to implement and use. All of the presented platforms offer free services to engineering students and hence are adopted in this chapter.² Along the way, we will cover the origins and capabilities of such platforms. Toward the end of this chapter, we will cover the use of Python and R as representatives of coding-based approaches. In all cases, I have supplemented my discussions with a series of examples and tutorials.³ I have listed and appended these here for you ...