The clamour of anticipation around new applications for artificial intelligence is as fevered as ever. The problem for me is that expectations are not informed by a robust appreciation of the practical requirements for innovating with AI. As an adviser to businesses on bringing such innovation to market, my advice is simple: to scale rapidly, large-scale AI-enabled projects must be built on firm foundations to allow multidisciplinary development teams to thrive. Chief among the reasons is that, in engineering terms, developing AI is a complex, non-linear process. Frankly, you can expend a great deal of time and effort with very little progress to show for it.

Mathematics forms the core of data science and machine learning. Thus, to be the best data scientist you can be, you must have a working understanding of the most relevant math. Getting started in data science is easy thanks to high-level libraries like Scikit-learn and Keras. But understanding the math behind the algorithms in these libraries opens an infinite number of possibilities up to you. From identifying modeling issues to inventing new and more powerful solutions, understanding the math behind it all can dramatically increasing the impact you can make over the course of your career.

You use high-level software libraries (e.g., scikit-learn, Keras, TensorFlow) to train or deploy machine learning algorithms, and would now like to understand the fundamentals underlying the abstractions, enabling you to expand your capabilities You're a software developer who would like to develop a firm foundation for the deployment of machine learning algorithms into production systems You're a data scientist who would like to reinforce your understanding of the subjects at the core of your professional discipline You're a data analyst or A.I. enthusiast who would like to become a data scientist or data/ML engineer, and so you're keen to deeply understand the field you're entering from the ground up (very wise of you!) You use high-level software libraries (e.g., scikit-learn, Keras, TensorFlow) to train or deploy machine learning algorithms, and would now like to understand the fundamentals underlying the abstractions, enabling you to expand your capabilities You're a software developer who would like to develop a firm foundation for the deployment of machine learning algorithms into production systems You're a data scientist who would like to reinforce your understanding of the subjects at the core of your professional discipline You're a data analyst or A.I. enthusiast who would like to become a data scientist or data/ML engineer, and so you're keen to deeply understand the field you're entering from the ground up (very wise of you!)