Machine Learning with R - Fourth Edition

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Being Successful with Machine Learning

An all-too-common problem in the field of machine learning occurs when students, with fresh excitement from learning the methods, struggle to apply what they’ve learned to real-world projects. Much as the beauty of a forest trail feels sinister in the darkness of night, code and methods that initially seemed straightforward feel daunting in the absence of a step-by-step roadmap. Without such a guide, the learning curve feels so much steeper and pitfalls appear deeper.

It is discouraging to think about the countless students that have been turned away from machine learning, due to the chasm between machine learning in theory and practice. Having worked in the field for over a decade, and having trained, ...