For many years, I have had the privilege of training aspiring Machine Learning and Deep Learning students, helping them transition from coders to effective AI practitioners. To date, I have trained more than 600 AI practitioners, with student backgrounds ranging impressively from medical doctors to high school graduates.
How do we achieve such success with such a diverse group? We bypass the traditional, theory-first hurdles and rely on the revolutionary educational resources created by Jeremy Howard ... the fastai online YouTube course and the definitive book, Deep Learning for Coders with fastai & PyTorch (which is also available on-line for free as a series of Jupyter notebooks).
Contents
The Core Philosophy: Deep Learning is for Everyone
The foundational premise of these materials is that Deep Learning is for Everyone. The course and book were designed to shatter the "pernicious myth" that one needs specialized credentials, such as a PhD or years of advanced mathematical training, to build effective AI systems.
The only prerequisite for starting this journey is knowing how to code, preferably in Python (even a year of experience is enough), along with basic high school math knowledge. This radically inclusive approach is why my student cohort is so varied—it targets domain experts and motivated coders, not just specialized academics.
If you don't know any Python programming at all then this course on Real Python is the best if found that will get you up to speed as quickly as possible. Some of my students have gone through this course in a week!
Our Top-Down, Code-First Methodology
We leverage the materials' acclaimed top-down, "whole game" teaching philosophy. This means we don't spend months slogging through abstract theory first. Instead, the curriculum focuses on immediate practical application:
• Start Building Now: Students immediately dive into executing a complete, working, state-of-the-art deep learning network to solve real-world problems. For instance, practitioners can achieve state-of-the-art accuracy on complex problems within the first 30 minutes of Chapter 2 of the book.
• Theory in Context: Theory, math, and internal mechanics are only covered contextually, revealing why things work after the student has the hands-on experience and the motivation to dig deeper.
• Progressive Depth: The course starts with high-level, expressive tools (fastai) and progressively dives deeper into the low-level foundations (PyTorch or pure Python), ensuring a complete understanding of implementation.
The Inverted Classroom: Learning by Doing
My training program utilizes an inverted classroom model perfectly suited to this top-down philosophy, encouraging tenacity and perseverance. My students access the fastai YouTube course and companion notebooks online, where they focus on "learning by doing". They are constantly experimenting, adjusting the provided code, and working through their own projects simultaneously. This self-paced, practice-heavy environment means students inevitably hit complex obstacles. That is where I come in: if they get stuck, they ask me for help. This mentorship component is crucial because, as the sources emphasize, deep learning mastery is artisanal. Proficiency comes only through practical experience and access to a supportive network. Students who face hurdles know they have a personal guide to help them rewind, troubleshoot, and perform the constant small experiments necessary to move forward.
Building the Next Generation of AI Leaders
The fastai approach isn't just about training algorithms; it's about making sure practitioners understand the "whole game," including critical topics like data ethics.
If you are a coder with domain expertise, you are exactly the kind of person the world needs to apply this powerful technology. My job is to ensure you don't take the painful, years-long path of slow theoretical study. We aim to take you from beginner to qualified practitioner in a matter of months.
Are you ready to stop waiting for a PhD and start building AI applications? The right approach is already here, and the results speak for themselves.