SDS 446: Getting Started in Machine Learning — with Jon Krohn

Learning Journeys

Jon Krohn shares his personal learning journey in machine learning, highlighting the ongoing nature of education in this field. He reflects on the complexities of concepts like support vector machines and deep reinforcement learning, admitting that while executing them is straightforward, understanding the underlying theories can be challenging 1. Jon finds excitement in the continuous learning process, stating, "there's always, always, always more to learn in machine learning, and that's part of why I find this field so bloody exciting" 2. This perspective underscores the importance of hands-on experience and the evolving nature of machine learning knowledge.

   

Staying Current

Staying current in machine learning requires strategic learning paths and resources. Jon recommends starting with foundational courses in linear algebra, calculus, and probability before advancing to machine learning itself 3. He suggests resources like the "Deep Learning" book by Ian Goodfellow and others, which is available for free online, and emphasizes the value of platforms like Dataquest for interactive learning. Jon also maintains a comprehensive list of free resources on his website, encouraging learners to explore and contribute to this growing repository.