Traditional data lifecycles have been number>data>intelligence. With enveloping of AI by data science, that lifecycle has become richer and a bit more interesting as data now tends to evolve form nascent states all the way to predictive and even generative states.

In broadest terms, building AI requires two macro-level steps: 1. learn the rules, 2. apply the rules. Everything you need to learn about AI elaborates around those two steps. From there, we will break it down to a slightly more complex set of steps and activities that you

Evolve your understanding of AI from a rough familiarity of buzzwords to a mastery of AI models from simple linear regression to cutting edge generative AI models.

Contextualizing your understanding of the most common AI models with well documented and carefully curated examples and common case studies from the life sciences will ease your transition into a savvy AI practitioner.

The culture led by the advent of Web 3.0 has exponentially grown the proclivity for practitioners of all kinds to share their work – including source code and documentation. And in few sectors is this practice more prevalent than in the field of AI. You will walk away from