Python has become the universal language for both ML/AI engineers and data scientists, courtesy of its rich ecosystem of libraries and frameworks. This course explores shared aspects of this ecosystem, providing a foundation that can be applied across both worlds.
ML/AI Engineering vs. Data Analysis/Science
While both fields employ many of the same Pythonic tools, their focus differs depending on the problems they tackle
ML/AI engineers work with domains such as computer vision, natural language processing, and reinforcement learning for perceptual, automation and control-oriented applications. Roboticists also typically build models for embedded and/or real-time systems.
Data analysts and scientists focus on predictive modeling, trend analysis, and decision support in domains such as business, finance, and climate science. Datasets are typically tabular or textual.
This course highlights the intersections within the ecosystem, giving you a base of knowledge relevant to both fields
Course Objectives
Main focus: Familiarity with standard Python tools for manipulating and visualizing data, and for ML/DL workflows